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Aerial Autonomy Stacks

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Based on this discussion in Discourse and our startup meeting, we can define an aerial autonomy stack as follows:

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An aerial autonomy robotics stack is a collection of building blocks that enable the development of autonomous aerial vehicles, by providing a modular and scalable architecture for sensing, perception, planning, and control tasks. It allows unmanned aerial vehicles to perform complex missions without human intervention, while accommodating different hardware configurations and simulation environments.

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Comparison

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From the paper

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Fernandez-Cortizas, Miguel, et al. "Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems." arXiv preprint arXiv:2303.18237 (2023).

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the following autonomy stack table was extracted and adapted.

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Flight stackOS/OCModularTested inMiddlewarelast updateMFROMAMPPO
Aerostack✓✓S,RIL,ROLROS10/2021✗✓✓✓✗
Aerostack2✓✓S,RIL,ROLROS 203/2023✓✓✓✓✓
AerialCore✓✓S,RIL,ROLROS03/2023✓✓✓✗✓
Agilicious✓✓S,RILROS03/2023✗✓✗✗✗
KumarRobotics✓✗S,RIL,ROLROS12/2022✗✓✗✓✗
CrazyChoir✓✗S,RILROS 202/2023✗✓✓✗✗
UAL✓✗S,RIL,ROLROS12/2022✓✗✗✓✗
XTDrone✓✓SROS03/2023✗✓✗✗✗
RotorS✓✓SROS07/2021✗✓✗✗✗
GAAS✓✓SROS10/2021✗✗✗✗✗
MRS AUV System✓✓S,RIL,ROLROS09/2023✓✓✓✓✗
Crazyswarm✓✗S,RILROS12/2022✗✓✓✗✗
Crazyswarm2✓✓S,RILROS 209/2023✗✓✓✗✓
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Abbrivations +* OS/OC: Open source or Open code +* S: Experiments in simulation +* RIL: Experiments in the lab +* ROL: Experiments outside the lab +* MF: Multi-frame +* RO: Rate output +* MA: Multi agent +* MP: Multi platform +* PO: Plugin oriented

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VIO packages

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Visual Inertial Odometry packages is an very important strategy of positioning within GPS deprived environments. Since UAVs can not use wheel odometry and heavily relient on cameras, this is one of the main drivers for autonomous exploration with these vehicles.

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Here is a list of VIO packages that people can use if they have a depth camera on their platform.

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Visual Odometry Datasets

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Working list autonomy stacks

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This is just a list of autonomy stacks with links, such that later we can add them to the overview.

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Working list:

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Partial autonomy packages

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A list of packages which don't comprise a full stack but do offer value on top of basic flight controller firmware.

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Indoor navigation packages

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Given the above Aerial Autonomy Stacks, the list below outlines specific implementations of indoor navigation software packages in ROS, running on aerial vehicle platforms. The list, though not exhaustive, provides a good overview of available off-the-shelf non-commercial software.

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Package nameOS/OCSensors requiredMiddlewareSimulatorPlatform/controllerLast updated
Ardupilot ROS✓LiDARROS 2GazeboIris coptor,Ardupilot02/2024
as2_behaviour_tree✓UnknownROS 2GazeboCrazyflie,DJI,Tello02/2024
Teach-Repeat-Replan✓Stereo cameraROS 1MockaFlyDJI N311/2020
rtabmap✓Stereo cameraROS 1GazeboPX405/2023
ORB_SLAM_3✓Mono/stereo cameraROS 1N/ABebop 206/2023
relative_nav✗Stereo cameraROS 1N/ARotorcraft04/2017
zephyr✓LiDARROS 1RotorS/GazeboAscTec Firefly11/2018
tum_ardrone✓Mono cameraROS 1N/AAR.Drone05/2014
kr_autonomous_flight✓Stereo camera/LiDAR/IMUROS 1GazeboPixhawk08/2023
px4_sim_ros2✓Stereo cameraROS 2GazeboPX404/2024
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Aerial Vehicle Types and Categories

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Usually, people tend to think about quadcopters when talking about aerial robotics, but there is actually a much larger variety of vehicle types available! Here's an attempt to list all of them if we can.

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Copters

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quadcopter

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Copters are aerial vehicles that use motors and propellers for their lift and maneuverability in 3D space.

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These usually are categorized into the following subcategories:

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  • Quadcopters: The most common aerial vehicle used. It is driven by (as the name already indicates) 4 propellers and 4 motors.
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  • Helicopters: Like manned helicopters, unmanned helicopters are driven by one main rotor on top and one smaller one on the side for stability and maneuverability.
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  • Hexacopters/Octocopters: Copters with 6 or 8 rotors/motor combinations, respectively.
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  • Tilt-Rotor Copters: Copters that have motor and rotor combos that can change orientation.
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  • Monocopters: Monocopters are copters that are only driven by a single rotor and motor combo.
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Fixed Wing

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fixed_wing

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Fixed-wing aerial vehicles use fixed wings for takeoff lift and control.

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These are the subcategories:

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  • Regular Fixed Wings: Like regular airplanes, fixed-wing UAVs have 2 wings with a form of propulsion.
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  • Hybrid / VTOL: These vehicles can take off like a quadcopter vertically but can transition and act like a fixed-wing vehicle for the remainder of the flight.
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  • Gliding UAVs: These are fixed-wing UAVs without any propulsion and are therefore reliant on wind and thermal currents.
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Flapping Wing

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flapping

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Flapping-wing aerial vehicles generate both lift and thrust by their flapping wings.

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There are two main subcategories:

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  • Ornithopters: These are the traditional bird-like flapping-wing UAVs. The flapping wings generate lift and thrust and an airplane-like tail with traditional control surfaces is used for stbility and flight control. Some minimal forward velocity needs to be maintained such the wings produce sufficient lift. Only some smaller vehicles demonstrate limited VTOL and hovering capability. These vehicles can be passively stabilized by the tail surfaces and do not require active stabilization, similar to fixed-wing UAVs.
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  • Tail-less flapping-wing UAVs: These vehicles fly like insects and hummingbirds - they can hover, take-off and land vertically, and fly in any direction (forward & backward, sideways, up & down). Lift/thrust is generated by the flapping wings, whose geometry and/or motion is adjustable for flight control. Like multicopters, these vehicles require active stabilization, typically by using an onboard IMU.
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Blimps

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blimp

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Blimps or airships can also be used as aerial vehicles as well. These vehicles stay buoyant by using a balloon with lighter-than-air gas. Maneuverability is either done with flapping wings or rotors.

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List to support vehicles per autopilot suite

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Each autopilot suite have their a wide range of supported vehicles, to which this itemized list is referenced to. Note that these lists also include non-aerial vehicles as well.

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Autopilot Suites

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There are several autopilot suites available for control-boards for aerial vehicles. This page mostly have an overview of all of those including some comparison factors in them as well.

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Autopilot suitestartup yearlatest versionOS licenceROS supportsupported vehicles
Ardupilot (GitHub)20094.5.2 (05/24)GPL 3.0yesCopters, Fixed wings, VTOL
Betaflight (GitHub)20154.5.0 (04/24)GPL 3.0noCopters
crazyflie-firmware (GitHub)20112024.2GPL 3.0yes*Quadcopters, Flapping wings
DJI autopilot (GitHub)20062023.9closedyesCopters, VTOLS
Paparazzi (GitHub)20036.3.0 (12/23)GPL 2.0noCopters, Fixed wings, VTOL
PX4 (GitHub)20091.14.0 (10/23)BSD 3-ClauseyesCopters, Fixed wings, VTOL
ROSflight (GitHub)20192.2.0 beta (09/23)BSD 3-ClauseyesCopters, fixed wings
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*community provided support

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Education and tutorials

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This page will contain a list of education courses or tutorials for aerial robotics. +This is a partly compilation of this ROS discourse thread and the May meeting about tutorials and education and some googling :)

+

This also includes input from a Linkedin post for a call of suggestions. This has been an adaption from the following Bitcraze Blogpost, and the overview presentation done for the ROS-aerial CWG meetings

+

Course explanation

+

This section explains some of the recourses in more detail.

+

Books

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There is this important resource which is the book titled ‘Small Unmanned Aircraft: Theory and Practice.’ This book has been written by Randy Beard and Tim McLain of Brigham Young University, and it covers everything from the absolute basics of coordinate frames and quadrotor dynamics to path planning and cameras. It is a must-read for anybody starting in UAVs and Aerial robotics.

+

The physical book can be found here: http://press.princeton.edu/titles/9632.html

+

The available PDFs can be accessed on GitHub: https://github.com/randybeard/uavbook

+

Online Courses on Aerial Robotics

+

This section shows online courses for aerial robotics with online instructor.

+

Coursera offers the ‘Robotics: Aerial Robotics’ course as part of the Robotics specialization. Taught by Prof. Vijay Kumar from Penn University, this 4-week course covers the mechanics and control of aerial vehicles using Matlab. It starts from 1 dimension and gradually progresses to the 3rd dimension in simulation. The course is part of a paid educational program, but you can audit the lessons for free.

+

Link: https://www.coursera.org/learn/robotics-flight

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Udacity has been offering a course on Aerial Vehicles for quite some time for the Flying car nano degree. The lessons are taught by top names in the industry and cover key aspects of Aerial Robotics, such as motion planning, controls, and estimation, with lab assignments involving a real drone. The course duration is 4 months, and access is available for a fee.

+

Link: https://www.udacity.com/course/flying-car-nanodegree–nd787

+

edX offers the 'ETHx: Autonomous Mobile Robots' course. Taught by Prof. Roland Siegwart, Dr. Davide Scaramuzza, Prof. Marco Hutter, Prof. Margarita Chli, Prof. Martin Rufli and Prof. Nicholas Lawrance from ETH Zurich, this 15-week course focuses on the principles of autonomous navigation and control of mobile robots. It covers topics such as perception, localization, planning, and control, which are essential for enabling robots to operate autonomously in dynamic environments. You can audit the course material for free with limited access.

+

Link: https://www.edx.org/learn/autonomous-robotics/eth-zurich-autonomous-mobile-robots?utm_campaign=social-sharing-course-page&utm_medium=social&utm_source=email

+

Additionally, there’s the course ‘Applied Control System 3: UAV Drone (3D Dynamics & Control)’ which is part of a series by Mark Misin. This course delves deep into the dynamics, control, and modeling of quadrotors.

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Link: https://www.udemy.com/course/applied-control-systems-for-engineers-2-uav-drone-control/

+

University courses on Aerial Robotics with open resources

+

This section showes university courses that have released recordings of lectures, slides and/or assignments. For instructions participants would need to follow the actual course at that one university.

+

The University of Maryland offers a course on Autonomous Aerial Robotics, making all videos, slides, and assignments available. Taught by Nitin J. Sanket and Chahat Deep Singh, the course covers everything from basic control and dynamics to full autonomy. It’s a comprehensive resource for aerial robotics. The course utilizes the Parrot Bebop 2.0, and while a Mocap system is required, you may explore the possibility of adapting the course to a different platform. ROS is also part of this course

+

Link: http://prg.cs.umd.edu/enae788m

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‘Visual Navigation For Autonomous Vehicles’ is a course available on MIT Open Courseware, taught by Prof. Luca Carlone. As the name implies, the course primarily focuses on autonomous navigation for any autonomous vehicle. It includes exercises where students implement vision algorithms on both ground robots and drones. Additionally, the course covers working with ROS and applying the knowledge to a simulated drone in Unity. The students also get to learn how to work with ROS

+

Link: https://ocw.mit.edu/courses/16-485-visual-navigation-for-autonomous-vehicles-vnav-fall-2020/

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The ‘Bio-inspired Robotics’ course at the University of Washington, led by Prof. Sawyer Fuller, explores the realm of drawing inspiration from nature rather than reinventing the wheel. It covers various robots inspired by creatures capable of swimming, walking, hopping, and of course, flying. Lab assignments in this course involve working with a Crazyflie drone.

+

Link: https://faculty.washington.edu/minster/bio_inspired_robotics/

+

Brown University offers a course called ‘Introduction to Robotics’ taught by Prof. Stefanie Tellix. While the introduction covers generic robotics, the focus of the full course is on building and programming the Duckiedrone. The course dives straight into autonomy and also teaches students how to work with ROS.

+

Link: https://cs.brown.edu/courses/cs1951r/

+

Princeton University have also decided to release their ‘Intro to Robotics’ lectures and materials for the public. It covers all from control and estimation, computer vision and planning. Also it offers lab assignments with the Crazyflie.

+

Link: https://irom-lab.princeton.edu/intro-to-robotics/

+

Youtube

+

Youtube also has quite some tutorials available so this section highlights a few.

+

Drone Programming with Python: This popular tutorial/course teaches viewers how to program a real drone using Python with the DJI Tello. It offers a great opportunity for anyone looking for a short and enjoyable project to undertake, especially on a rainy day, while still working with a real platform.

+

Link: https://youtu.be/LmEcyQnfpDA

+

Intelligent Quads YouTube Channel: This channel is entirely dedicated to creating autonomous UAVs, covering topics from Ardupilot to MAVlink to ROS and Gazebo. It appears to be a valuable resource for beginners in the field of autonomous UAVs. This also includes ROS as part of the lessons as well.

+

Link: https://www.youtube.com/@IntelligentQuads

+

Code Examples

+

Here are some code examples that can be used as reference for experiments.

+

Mambo ROS Examples: This is a collection of experiments targeted Parrot Mambo drones, there are experiments with one or multiple vehicles at the same time. It runs over ROS over BLE, and some test are adapted to make use of a Vicon MoCap system. From a control theory's perspective, it showcases an optional robust control strategy, using an H-Infinity controller with perturbation estimation and a identified dynamic model of a Parrot Mambo drone.

+

Link: https://github.com/TOTON95/Mambo_ROS_Examples

+

Some special mentions

+

So here there are some courses that either doesn't fit in the above categories or are deprecated.

+ +

Working list

+

This list contains some resources that we haven't included in the overview. Remove the item once it has been included

+ +

Credit

+

Lots of thanks for anybody contributing to this linkedin post. This was extremely helpful!

+ + +
+
+ + +
+ +
+ +

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+
+ + Built with MkDocs using a theme provided by Read the Docs. +
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + + Next » + + +
+ + + + + + + + + diff --git a/goatcounter.html b/goatcounter.html new file mode 100644 index 0000000..9d12a66 --- /dev/null +++ b/goatcounter.html @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/hardware/index.html b/hardware/index.html new file mode 100644 index 0000000..f2aefdb --- /dev/null +++ b/hardware/index.html @@ -0,0 +1,441 @@ + + + + + + + + Hardware, Components, and Dev Kits - Aerial Robotics Landscape + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

Hardware, Components, and Dev Kits

+

This is an list of development platforms for aerial robotics. Please start a pull request if you'd like to update these.

+

Standard Commercial Research Platforms

+

These are platforms that are currently commercially available for anybody to buy for their research.

+

Copters

+ +

Flapping wing

+ +

Industrial Platforms

+ +

Inhouse-developed platforms

+

These are platforms that are standard within a lab or department, with information of what it contains provided with perhaps build instructions.,

+ +

Discontinued Platforms

+ +

Components

+

Many of the UAVs are usually built by hand and composed of different components. This usually consists of a drone frame, flight controller boards, companion computers and of course motors, batteries and ESCs.

+

Drone Frames

+

Many drone frames are usually built from carbon fiber and custom-made for application or research. +There are some frames that are provided that will provide some base: +- DJI Flame wheel ARF kit F550, F450, F330 +- Momentum Drones DEV-7

+

Flight controllers

+ +

Companion Computers

+

For the drones that can carry it, the companion computers are important since they can do additional computations that the flight controller can not easily do. +As these are capable of running some form of Linux, these can handle for instance computer vision with OpenCV or run nodes with ROS. +Some companion computers also integrate flight control (RTOS) hardware in the same package

+ +

Carrier boards

+

Several vendors have developed carrier boards that can expose input/output ports of companion computers mentioned above which are packaged in a System-on-Module (SoM) form factor and also offer a standard interface for plugging in popular flight controllers/their own FCs.

+ +

Depth Cameras

+ +

Monocular Cameras

+
    +
  • Raspberry PI Cameras Compatible with RPI. IMX219 and IMX477 based cameras compatible with Jetson Xavier, NX, Nano, Orin series (other models may require device tree overlays or source changes).
    • +
  • Google Coral Camera Compatible with NXP NAVQPLUS
    • +
+

TOF Cameras

+ +

Reference Bill of Materials

+

Holybro S500v2

+

The Holybro S500v2 is a popular, relatively low-cost quadcopter. This is a list of the parts used with details of battery, motors, ESCs, and propellers with reference links to guide custom builds.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
S.NoPart NamePart categoryDescriptionPrice (USD)QtyTotal Cost (USD)Official/Reference Link
1Holybro S500 frameFrameWith landing gear, 385x385mm42142https://holybro.com/collections/s500/products/s500-v2-kit?variant=42724497391805
2Holybro Pixhawk 6C + GPS + Power moduleFC + GPS + Power modulePM02 power module, M9N GPS2901290https://holybro.com/products/pixhawk-6c?variant=43005243785405
3Holybro 2216 920KV CWMotor19x19 mounting clockwise rotation20240https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094608061
4Holybro 2216 920KV CCWMotor19x19 mounting counter-clockwise rotation20240https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094640829
5BLHeli S 20A ESCESCElectronic Speed Controller to drive motors14456https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094706365
61045 propellersProps10x4.5" kit of 2 pairs12112https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094313149
7Radiomaster R81 receiverRadio receiverUsed for manually flying drone Line of Sight/testing*18118https://holybro.com/products/radiomaster-r81-receiver
8Holybro SiK telemetry radio v3Telemetry link radio500mW, 433MHz variant, pair of transmitter + receiver**63163https://holybro.com/products/sik-telemetry-radio-v3?variant=42801817485501
9Tattu 5200mAh 4SBattery4S1P XT60 plug 35C63163https://genstattu.com/tattu-5200mah-14-8v-35c-4s1p-lipo-battery-pack-with-xt60-plug.html
Grand Total624
+

Notes:

+

*It can be used with Radiomaster Multiprotocol (4 in 1) or CC2500 based Radio Controller like FrSky Taranis X9D or similar
+**Used for connecting to ground control station, 915MHz variant also available

+

Useful tool for this page: +https://tabletomarkdown.com/convert-spreadsheet-to-markdown/

+ + +
+
+ + +
+ +
+ +

This website is information assembled with help of the ROS Aerial Robotics Community group. Analytics with no-cookie tracking with goatviewer.com

+
+ + Built with MkDocs using a theme provided by Read the Docs. +
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/images/blimp.png b/images/blimp.png new file mode 100644 index 0000000..b8bf6d0 Binary files /dev/null and b/images/blimp.png differ diff --git a/images/edit-button.png b/images/edit-button.png new file mode 100644 index 0000000..5aafe3e Binary files /dev/null and b/images/edit-button.png differ diff --git a/images/fixed_wing.png b/images/fixed_wing.png new file mode 100644 index 0000000..01727a4 Binary files /dev/null and b/images/fixed_wing.png differ diff --git a/images/flapping.png b/images/flapping.png new file mode 100644 index 0000000..d2cde7c Binary files /dev/null and b/images/flapping.png differ diff --git a/images/quadcopter.png b/images/quadcopter.png new file mode 100644 index 0000000..5ac4aec Binary files /dev/null and b/images/quadcopter.png differ diff --git a/img/favicon.ico b/img/favicon.ico new file mode 100644 index 0000000..e85006a Binary files /dev/null and b/img/favicon.ico differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..5fe20e9 --- /dev/null +++ b/index.html @@ -0,0 +1,196 @@ + + + + + + + + Aerial Robotics Landscape + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

Aerial Robotic Landscape

+

build

+

This repository/website is a collection of resources for aerial robotics. It is intended to be a comprehensive list of recourses for anyone interested in aerial robotics. The resources are divided into categories, such as autonomy stacks, autopilot suites, simulation, message standards, safety and management systems, tutorials and education, hardware, components, and development kits, aerial vehicle types, and middleware and drivers.

+
+

Contributions are welcomed

+

This project is under active development and you can contribute to it, by submiting a PR with you changes or posting an issue to the repository in Github.

+
+

Community

+

This landscape is a community project, and we encourage readers to +contribute to the landscape. The best way to contribute is to join our +bi-weekly meetings, and/or add to the lists.

+

The main repository for the Community Work Group

+

Meetings

+

The Aerial Robotics group meets bi-weekly on Wednesday's at 16:00 UTC. +Meetings will be announced on the ROS Discourse Aerial Robotics forum category at least a week in advance, and recordings and meeting notes will be made available after each meeting.

+

Team / Community Coordination

+ +

Contributing

+

Contributions to the landscape are highly encouraged, from simple fixes +to spelling and grammer all the way to adding new sections to the +landscape.

+

Quick Changes in Github

+

Simple changes to existing pages can be done by clicking the "Edit on +Github" link that appears at the top of every page.

+

edit button

+

To edit an existing page: +1. Open the page. +2. Click the Edit on Github link on top of the page. +3. Make the changes in the online editor. +4. Below the Github page editor you'll be prompted to create a separate branch and then guided to submit a pull request.

+

Once you submit the team will review your change request and either +provide feedback on how to improve or just merge it, resulting in the +website getting updated.

+

Changes using Git (New Pages and Images)

+

More substantial changes, including adding new pages or adding/modifying images, aren't as easy to make (or properly test) on Github. For these kinds of changes we suggest using the same approach as for code:

+
    +
  1. Use the git toolchain to get the documentation source code onto your local computer with git clone git@github.com:ROS-Aerial/aerial_robotic_landscape.git
    2. +
  2. Modify the documentation as needed (add, change, delete).
    3. +
  3. Test that it builds properly with mkdocs. Check out their documentation for building the website and trying the website locally.
    4. +
  4. Create a branch for your changes and create a pull request (PR) to pull it back into the documentation.
    5. +
+ + +
+
+ +
+ +
+ +

This website is information assembled with help of the ROS Aerial Robotics Community group. Analytics with no-cookie tracking with goatviewer.com

+
+ + Built with MkDocs using a theme provided by Read the Docs. +
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + + +
+ + + + + + + + + + + diff --git a/js/html5shiv.min.js b/js/html5shiv.min.js new file mode 100644 index 0000000..1a01c94 --- /dev/null +++ b/js/html5shiv.min.js @@ -0,0 +1,4 @@ +/** +* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed +*/ +!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return 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+ + +
+ +
+
+ +
+
+
+
+ +

Message standards

+

REP 147 A Standard interface for Aerial Vehicles

+

Within the ROS ecoysystem, there is currently a ROS enhancement proposal (REP) available to give guidance to message standards for UAVS. This can be found here:

+

REP 147 A Standard interface for Aerial Vehicles

+

Message standards within autopilot suites

+ +

MAVLink, an acronym for Micro Air Vehicle Link, is a communication system mainly used for the exchange of information between unmanned aerial vehicles and ground control stations. +This protocol, which was launched in 2009, is structured as a header-only message marshaling library. +MAVLink is versatile, supporting a broad range of messages and capable of being sent over virtually any type of serial connection, including Wi-Fi and radio technologies. +In terms of messages, they are defined at compile time in XML files, which are processed to create libraries for sending and receiving messages. +If new messages are added or existing ones are modified, the XML definitions need to be updated and the application recompiled.

+

https://mavlink.io/en/

+

MAVlink is used in 2 autopilot suites. Eventhough the same message type are being shared in both the suites, the accomendating behavior might differ:

+ + + +
+
+ + +
+ +
+ +

This website is information assembled with help of the ROS Aerial Robotics Community group. Analytics with no-cookie tracking with goatviewer.com

+
+ + Built with MkDocs using a theme provided by Read the Docs. +
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/middleware_and_drivers/index.html b/middleware_and_drivers/index.html new file mode 100644 index 0000000..a178f35 --- /dev/null +++ b/middleware_and_drivers/index.html @@ -0,0 +1,178 @@ + + + + + + + + Middleware and Drivers - Aerial Robotics Landscape + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

Middleware and Drivers

+

The aim for this page is to provide a collection of ROS packages/related libraries which are very useful for aerial ROS projects either to serve as a reference or directly use as a part of a custom software stack. This list aims to address the gap between the flight controller firmware and purely autonomy related packages.

+

Middleware libraries

+

Enabling low overhead publish/subscribe on microcontrollers:

+
    +
  1. Micro-XRCE-DDS
    2. +
  2. Zenoh
    3. +
  3. PX4-FastRTPS - superceded by Micro-XRCE-DDS (https://docs.px4.io/main/en/middleware/uxrce_dds.html)
    4. +
+

Driver packages for drone platforms

+

ROS packages built atop SDKs from drone vendors to interface to their closed-source flight controller firmwares. Several of these may be for much older ROS1 distros but can have utility in terms of serving as references.

+
    +
  1. Parrot ARDrone Autonomy
    2. +
  2. Parrot Bebop Autonomy
    3. +
  3. DJI Tello driver
    4. +
  4. Parrot Mambo driver
    5. +
+ + +
+
+ + +
+ +
+ +

This website is information assembled with help of the ROS Aerial Robotics Community group. Analytics with no-cookie tracking with goatviewer.com

+
+ + Built with MkDocs using a theme provided by Read the Docs. +
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/safety_management/index.html b/safety_management/index.html new file mode 100644 index 0000000..396e118 --- /dev/null +++ b/safety_management/index.html @@ -0,0 +1,240 @@ + + + + + + + + Safety and Management Systems - Aerial Robotics Landscape + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

Safety and Management Systems

+

Quadcopter and other Aerial vehicles come in all forms and sizes. +Safety systems are important for all, but the bigger an aerial vehicle becomes, the more important it becomes that the right fail-safes and emergency systems are in place! +This pages gives an overview of those safety systems that have been implemented so far.

+

Safety in ROS

+

Currently specifically for aerial vehicles, the safety implementation is depended on the ROS autonomy stack used.

+

The ROS-0147 has a suggestion for flightmodes for a statemachine that can simplify the understanding of it. It is also expecting that ROS will be run offboard on an external computer, and that it should be in control of the safety state machine. This should probably be discussed if it would be a good idea for ROS to be in control of this or the autopilot suites themselves.

+

Safety in Autopilot suites

+

Adapted from the following blogpost: https://www.bitcraze.io/2023/04/safety-and-the-brushless/

+

These are current Safety managements systems existing in Paparazzi UAV, Betaflight, ArduPilot and PX4. +The Crazyflie ecosystem also have some measures but are currently overhauling their safety framework now in the form of a supervisor.

+

Pre-flight checks.

+

Before a vehicle can fly, certain conditions must be met. These includ:

+
    +
  • calibrating and ensuring functionality of internal sensors (IMU, barometer, magnetometer)
    • +
  • receiving a GPS signal
    • +
  • converging the internal state estimator (usually an extended Kalman filter) to a position
    • +
  • checking for remote control connection and datalink to a ground station
    • +
  • conducting feasibility checks (e.g., mission parameters, start location proximity)
    • +
  • confirming sufficient battery level, and ensuring the absence of error states from previous flights or crashes.
    • +
+

Preflight checks documentation:

+ +

Fail-safe mechanisms

+

After passing pre-flight checks and arming the UAV, the takeoff command is given. However, UAV flights have inherent risks, particularly during takeoff. To mitigate these risks, numerous safety features, known as failsafes, are implemented during the flight phase. These failsafes are categorized as triggers and behaviors, allowing developers to specify the UAV's response to different failures, such as initiating a safe landing in the event of GPS loss.

+

Triggers

+

Thus, there are triggers that can enable the autopilot’s failsafe mechanics:

+
    +
  • No connection with the remote control
    • +
  • No connection with the Ground station or Datalink
    • +
  • Low Battery
    • +
  • Position estimate diverges or full GPS loss
    • +
  • Waypoint going beyond geofence or Mission is not feasible
    • +
  • Other vehicles are nearby.
    • +
+

Also, sometimes the support of an external Automatic Trigger system is required, which is a box that monitors the conditions where the UAV should take action in case there is no GPS, other aerial vehicles are nearby, or the UAV is crossing a geofence determined by outdoor flight restrictions. Note that all of these triggers usually have a couple of conditions attached, such as the level of the ‘low battery’ or the number of seconds of ‘GPS loss’ deemed acceptable.

+

Fail-safe behaviors

+

During UAV flights, safety features can be customized per trigger, deviating from the default actions set by regulations. Disarming the vehicle completely increases the risk of crashing and causing harm. Allowing the drone to autonomously complete the mission without intervention poses the risk of losing the vehicle or trespassing restricted areas. Modifying default behaviors should be undertaken by knowledgeable individuals with careful consideration.

+

These behaviors can include the following:

+
    +
  • No action at all
    • +
  • Warning on the console or remote control display
    • +
  • Continue the mission autonomously
    • +
  • Stay still at the same position or go to a home position
    • +
  • Fly to a lower altitude
    • +
  • Land based on position or safely land by reducing thrust
    • +
  • No input to motors or completely disarming the motors
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Fail-safe documentation

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Emergencies

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Fail-safes ensure safe flight, but emergencies like crashes, flips, or hardware failures can still occur. In such cases, the standard default action is to disarm the vehicle to prevent unintended motor activation. Backup systems connected to ESCs may take over if the autopilot becomes unresponsive. The pilot plays a vital role in safety, with the remote control featuring a dedicated button or switch for different modes, enabling actions like landing or disarming. It's recommended to have a net or towel to stop spinning motors and to promptly disconnect the battery. Being prepared for potential LiPo battery hazards is essential, with sand or fire retardant on hand. While autopilots provide guidance, conducting thorough research on handling emergencies, spinning parts, and LiPo battery fires is crucial.

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Here a list of that: +* Remote control should have a dedicated button/switch for different modes, landing, or disarming. +* Dealing with spinning motors Use a net or towel to stop them and promptly disconnect the battery. +* To prepare for LiPo battery hazards, Have sand or fire retardant available.

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+ + + + + + + + + diff --git a/search/lunr.js b/search/lunr.js new file mode 100644 index 0000000..aca0a16 --- /dev/null +++ b/search/lunr.js @@ -0,0 +1,3475 @@ +/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */ + +;(function(){ + +/** + * A convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.3.9" +/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + * @namespace lunr.utils + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf lunr.utils + * @function + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf lunr.utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} + +/** + * Clones an object. + * + * Will create a copy of an existing object such that any mutations + * on the copy cannot affect the original. + * + * Only shallow objects are supported, passing a nested object to this + * function will cause a TypeError. + * + * Objects with primitives, and arrays of primitives are supported. + * + * @param {Object} obj The object to clone. + * @return {Object} a clone of the passed object. + * @throws {TypeError} when a nested object is passed. + * @memberOf Utils + */ +lunr.utils.clone = function (obj) { + if (obj === null || obj === undefined) { + return obj + } + + var clone = Object.create(null), + keys = Object.keys(obj) + + for (var i = 0; i < keys.length; i++) { + var key = keys[i], + val = obj[key] + + if (Array.isArray(val)) { + clone[key] = val.slice() + continue + } + + if (typeof val === 'string' || + typeof val === 'number' || + typeof val === 'boolean') { + clone[key] = val + continue + } + + throw new TypeError("clone is not deep and does not support nested objects") + } + + return clone +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/*! + * lunr.Set + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A lunr set. + * + * @constructor + */ +lunr.Set = function (elements) { + this.elements = Object.create(null) + + if (elements) { + this.length = elements.length + + for (var i = 0; i < this.length; i++) { + this.elements[elements[i]] = true + } + } else { + this.length = 0 + } +} + +/** + * A complete set that contains all elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.complete = { + intersect: function (other) { + return other + }, + + union: function () { + return this + }, + + contains: function () { + return true + } +} + +/** + * An empty set that contains no elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.empty = { + intersect: function () { + return this + }, + + union: function (other) { + return other + }, + + contains: function () { + return false + } +} + +/** + * Returns true if this set contains the specified object. + * + * @param {object} object - Object whose presence in this set is to be tested. + * @returns {boolean} - True if this set contains the specified object. + */ +lunr.Set.prototype.contains = function (object) { + return !!this.elements[object] +} + +/** + * Returns a new set containing only the elements that are present in both + * this set and the specified set. + * + * @param {lunr.Set} other - set to intersect with this set. + * @returns {lunr.Set} a new set that is the intersection of this and the specified set. + */ + +lunr.Set.prototype.intersect = function (other) { + var a, b, elements, intersection = [] + + if (other === lunr.Set.complete) { + return this + } + + if (other === lunr.Set.empty) { + return other + } + + if (this.length < other.length) { + a = this + b = other + } else { + a = other + b = this + } + + elements = Object.keys(a.elements) + + for (var i = 0; i < elements.length; i++) { + var element = elements[i] + if (element in b.elements) { + intersection.push(element) + } + } + + return new lunr.Set (intersection) +} + +/** + * Returns a new set combining the elements of this and the specified set. + * + * @param {lunr.Set} other - set to union with this set. + * @return {lunr.Set} a new set that is the union of this and the specified set. + */ + +lunr.Set.prototype.union = function (other) { + if (other === lunr.Set.complete) { + return lunr.Set.complete + } + + if (other === lunr.Set.empty) { + return this + } + + return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * Optional metadata can be passed to the tokenizer, this metadata will be cloned and + * added as metadata to every token that is created from the object to be tokenized. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @param {?object} metadata - Optional metadata to associate with every token + * @returns {lunr.Token[]} + * @see {@link lunr.Pipeline} + */ +lunr.tokenizer = function (obj, metadata) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token( + lunr.utils.asString(t).toLowerCase(), + lunr.utils.clone(metadata) + ) + }) + } + + var str = obj.toString().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + var tokenMetadata = lunr.utils.clone(metadata) || {} + tokenMetadata["position"] = [sliceStart, sliceLength] + tokenMetadata["index"] = tokens.length + + tokens.push( + new lunr.Token ( + str.slice(sliceStart, sliceEnd), + tokenMetadata + ) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null, undefined or an empty string. This token will not be passed to any downstream pipeline + * functions and will not be added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === null || result === void 0 || result === '') continue + + if (Array.isArray(result)) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @param {?object} metadata - Optional metadata to associate with the token + * passed to the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str, metadata) { + var token = new lunr.Token (str, metadata) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the similarity between this vector and another vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / this.magnitude() || 0 +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2020 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + * @function + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @function + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @function + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } + + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + + if (frame.editsRemaining == 0) { + continue + } + + // insertion + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } + + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.str.length > 1) { + stack.push({ + node: frame.node, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // deletion + // just removing the last character from the str + if (frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } + + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } + + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * When a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * This is not intended to be used on a TokenSet that + * contains wildcards, in these cases the results are + * undefined and are likely to cause an infinite loop. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + /* In Safari, at this point the prefix is sometimes corrupted, see: + * https://github.com/olivernn/lunr.js/issues/279 Calling any + * String.prototype method forces Safari to "cast" this string to what + * it's supposed to be, fixing the bug. */ + frame.prefix.charAt(0) + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.fieldVectors - Field vectors + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * Each term also supports a presence modifier. By default a term's presence in document is optional, however + * this can be changed to either required or prohibited. For a term's presence to be required in a document the + * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and + * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not + * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + * @example terms with presence modifiers + * -foo +bar baz + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. For details on how the score is calculated, please see + * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null), + requiredMatches = Object.create(null), + prohibitedMatches = Object.create(null) + + /* + * To support field level boosts a query vector is created per + * field. An empty vector is eagerly created to support negated + * queries. + */ + for (var i = 0; i < this.fields.length; i++) { + queryVectors[this.fields[i]] = new lunr.Vector + } + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null, + clauseMatches = lunr.Set.empty + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term, { + fields: clause.fields + }) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + /* + * If a term marked as required does not exist in the tokenSet it is + * impossible for the search to return any matches. We set all the field + * scoped required matches set to empty and stop examining any further + * clauses. + */ + if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = lunr.Set.empty + } + + break + } + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field, + matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) + + /* + * if the presence of this term is required ensure that the matching + * documents are added to the set of required matches for this clause. + * + */ + if (clause.presence == lunr.Query.presence.REQUIRED) { + clauseMatches = clauseMatches.union(matchingDocumentsSet) + + if (requiredMatches[field] === undefined) { + requiredMatches[field] = lunr.Set.complete + } + } + + /* + * if the presence of this term is prohibited ensure that the matching + * documents are added to the set of prohibited matches for this field, + * creating that set if it does not yet exist. + */ + if (clause.presence == lunr.Query.presence.PROHIBITED) { + if (prohibitedMatches[field] === undefined) { + prohibitedMatches[field] = lunr.Set.empty + } + + prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) + + /* + * Prohibited matches should not be part of the query vector used for + * similarity scoring and no metadata should be extracted so we continue + * to the next field + */ + continue + } + + /* + * The query field vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + + /** + * If the presence was required we need to update the requiredMatches field sets. + * We do this after all fields for the term have collected their matches because + * the clause terms presence is required in _any_ of the fields not _all_ of the + * fields. + */ + if (clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) + } + } + } + + /** + * Need to combine the field scoped required and prohibited + * matching documents into a global set of required and prohibited + * matches + */ + var allRequiredMatches = lunr.Set.complete, + allProhibitedMatches = lunr.Set.empty + + for (var i = 0; i < this.fields.length; i++) { + var field = this.fields[i] + + if (requiredMatches[field]) { + allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) + } + + if (prohibitedMatches[field]) { + allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + /* + * If the query is negated (contains only prohibited terms) + * we need to get _all_ fieldRefs currently existing in the + * index. This is only done when we know that the query is + * entirely prohibited terms to avoid any cost of getting all + * fieldRefs unnecessarily. + * + * Additionally, blank MatchData must be created to correctly + * populate the results. + */ + if (query.isNegated()) { + matchingFieldRefs = Object.keys(this.fieldVectors) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + var matchingFieldRef = matchingFieldRefs[i] + var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) + matchingFields[matchingFieldRef] = new lunr.MatchData + } + } + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef + + if (!allRequiredMatches.contains(docRef)) { + continue + } + + if (allProhibitedMatches.contains(docRef)) { + continue + } + + var fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = Object.create(null), + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = Object.create(null) + this._documents = Object.create(null) + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * A function that is used to extract a field from a document. + * + * Lunr expects a field to be at the top level of a document, if however the field + * is deeply nested within a document an extractor function can be used to extract + * the right field for indexing. + * + * @callback fieldExtractor + * @param {object} doc - The document being added to the index. + * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. + * @example Extracting a nested field + * function (doc) { return doc.nested.field } + */ + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * Fields can be boosted at build time. This allows terms within that field to have more + * importance when ranking search results. Use a field boost to specify that matches within + * one field are more important than other fields. + * + * @param {string} fieldName - The name of a field to index in all documents. + * @param {object} attributes - Optional attributes associated with this field. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. + * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. + * @throws {RangeError} fieldName cannot contain unsupported characters '/' + */ +lunr.Builder.prototype.field = function (fieldName, attributes) { + if (/\//.test(fieldName)) { + throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") + } + + this._fields[fieldName] = attributes || {} +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * Entire documents can be boosted at build time. Applying a boost to a document indicates that + * this document should rank higher in search results than other documents. + * + * @param {object} doc - The document to add to the index. + * @param {object} attributes - Optional attributes associated with this document. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. + */ +lunr.Builder.prototype.add = function (doc, attributes) { + var docRef = doc[this._ref], + fields = Object.keys(this._fields) + + this._documents[docRef] = attributes || {} + this.documentCount += 1 + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i], + extractor = this._fields[fieldName].extractor, + field = extractor ? extractor(doc) : doc[fieldName], + tokens = this.tokenizer(field, { + fields: [fieldName] + }), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < fields.length; k++) { + posting[fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + var fields = Object.keys(this._fields) + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i] + accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + fieldName = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + + var fieldBoost = this._fields[fieldName].boost || 1, + docBoost = this._documents[fieldRef.docRef].boost || 1 + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) + score *= fieldBoost + score *= docBoost + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: Object.keys(this._fields), + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata || {}) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + + if (term !== undefined) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata + } +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ + +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * Constants for indicating what kind of presence a term must have in matching documents. + * + * @constant + * @enum {number} + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with required presence + * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) + */ +lunr.Query.presence = { + /** + * Term's presence in a document is optional, this is the default value. + */ + OPTIONAL: 1, + + /** + * Term's presence in a document is required, documents that do not contain + * this term will not be returned. + */ + REQUIRED: 2, + + /** + * Term's presence in a document is prohibited, documents that do contain + * this term will not be returned. + */ + PROHIBITED: 3 +} + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. + * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + if (!('presence' in clause)) { + clause.presence = lunr.Query.presence.OPTIONAL + } + + this.clauses.push(clause) + + return this +} + +/** + * A negated query is one in which every clause has a presence of + * prohibited. These queries require some special processing to return + * the expected results. + * + * @returns boolean + */ +lunr.Query.prototype.isNegated = function () { + for (var i = 0; i < this.clauses.length; i++) { + if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { + return false + } + } + + return true +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion + * to a token or token-like string should be done before calling this method. + * + * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an + * array, each term in the array will share the same options. + * + * @param {object|object[]} term - The term(s) to add to the query. + * @param {object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + * @example using lunr.tokenizer to convert a string to tokens before using them as terms + * query.term(lunr.tokenizer("foo bar")) + */ +lunr.Query.prototype.term = function (term, options) { + if (Array.isArray(term)) { + term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) + return this + } + + var clause = options || {} + clause.term = term.toString() + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' +lunr.QueryLexer.PRESENCE = 'PRESENCE' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + // "+" indicates term presence is required + // checking for length to ensure that only + // leading "+" are considered + if (char == "+" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + // "-" indicates term presence is prohibited + // checking for length to ensure that only + // leading "-" are considered + if (char == "-" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseClause + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseClause = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.PRESENCE: + return lunr.QueryParser.parsePresence + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parsePresence = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.str) { + case "-": + parser.currentClause.presence = lunr.Query.presence.PROHIBITED + break + case "+": + parser.currentClause.presence = lunr.Query.presence.REQUIRED + break + default: + var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term or field, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/search/main.js b/search/main.js new file mode 100644 index 0000000..a5e469d --- /dev/null +++ b/search/main.js @@ -0,0 +1,109 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function escapeHtml (value) { + return value.replace(/&/g, '&') + .replace(/"/g, '"') + .replace(//g, '>'); +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + var noResultsText = search_results.getAttribute('data-no-results-text'); + if (!noResultsText) { + noResultsText = "No results found"; + } + search_results.insertAdjacentHTML('beforeend', '

' + noResultsText + '

'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 0000000..f76f608 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Aerial Robotic Landscape This repository/website is a collection of resources for aerial robotics. It is intended to be a comprehensive list of recourses for anyone interested in aerial robotics. The resources are divided into categories, such as autonomy stacks, autopilot suites, simulation, message standards, safety and management systems, tutorials and education, hardware, components, and development kits, aerial vehicle types, and middleware and drivers. Contributions are welcomed This project is under active development and you can contribute to it, by submiting a PR with you changes or posting an issue to the repository in Github. Community This landscape is a community project, and we encourage readers to contribute to the landscape. The best way to contribute is to join our bi-weekly meetings, and/or add to the lists. The main repository for the Community Work Group Meetings The Aerial Robotics group meets bi-weekly on Wednesday's at 16:00 UTC. Meetings will be announced on the ROS Discourse Aerial Robotics forum category at least a week in advance, and recordings and meeting notes will be made available after each meeting. Team / Community Coordination Instant Messaging ROS Discord: under the #cwg-aerial Forums ROS Discourse Aerial Robotics Category Contributing Contributions to the landscape are highly encouraged, from simple fixes to spelling and grammer all the way to adding new sections to the landscape. Quick Changes in Github Simple changes to existing pages can be done by clicking the \"Edit on Github\" link that appears at the top of every page. To edit an existing page: 1. Open the page. 2. Click the Edit on Github link on top of the page. 3. Make the changes in the online editor. 4. Below the Github page editor you'll be prompted to create a separate branch and then guided to submit a pull request. Once you submit the team will review your change request and either provide feedback on how to improve or just merge it, resulting in the website getting updated. Changes using Git (New Pages and Images) More substantial changes, including adding new pages or adding/modifying images, aren't as easy to make (or properly test) on Github. For these kinds of changes we suggest using the same approach as for code: Use the git toolchain to get the documentation source code onto your local computer with git clone git@github.com:ROS-Aerial/aerial_robotic_landscape.git Modify the documentation as needed (add, change, delete). Test that it builds properly with mkdocs . Check out their documentation for building the website and trying the website locally. Create a branch for your changes and create a pull request (PR) to pull it back into the documentation.","title":"Aerial Robotic Landscape"},{"location":"#aerial-robotic-landscape","text":"This repository/website is a collection of resources for aerial robotics. It is intended to be a comprehensive list of recourses for anyone interested in aerial robotics. The resources are divided into categories, such as autonomy stacks, autopilot suites, simulation, message standards, safety and management systems, tutorials and education, hardware, components, and development kits, aerial vehicle types, and middleware and drivers. Contributions are welcomed This project is under active development and you can contribute to it, by submiting a PR with you changes or posting an issue to the repository in Github.","title":"Aerial Robotic Landscape"},{"location":"#community","text":"This landscape is a community project, and we encourage readers to contribute to the landscape. The best way to contribute is to join our bi-weekly meetings, and/or add to the lists. The main repository for the Community Work Group","title":"Community"},{"location":"#meetings","text":"The Aerial Robotics group meets bi-weekly on Wednesday's at 16:00 UTC. Meetings will be announced on the ROS Discourse Aerial Robotics forum category at least a week in advance, and recordings and meeting notes will be made available after each meeting.","title":"Meetings"},{"location":"#team-community-coordination","text":"Instant Messaging ROS Discord: under the #cwg-aerial Forums ROS Discourse Aerial Robotics Category","title":"Team / Community Coordination"},{"location":"#contributing","text":"Contributions to the landscape are highly encouraged, from simple fixes to spelling and grammer all the way to adding new sections to the landscape.","title":"Contributing"},{"location":"#quick-changes-in-github","text":"Simple changes to existing pages can be done by clicking the \"Edit on Github\" link that appears at the top of every page. To edit an existing page: 1. Open the page. 2. Click the Edit on Github link on top of the page. 3. Make the changes in the online editor. 4. Below the Github page editor you'll be prompted to create a separate branch and then guided to submit a pull request. Once you submit the team will review your change request and either provide feedback on how to improve or just merge it, resulting in the website getting updated.","title":"Quick Changes in Github"},{"location":"#changes-using-git-new-pages-and-images","text":"More substantial changes, including adding new pages or adding/modifying images, aren't as easy to make (or properly test) on Github. For these kinds of changes we suggest using the same approach as for code: Use the git toolchain to get the documentation source code onto your local computer with git clone git@github.com:ROS-Aerial/aerial_robotic_landscape.git Modify the documentation as needed (add, change, delete). Test that it builds properly with mkdocs . Check out their documentation for building the website and trying the website locally. Create a branch for your changes and create a pull request (PR) to pull it back into the documentation.","title":"Changes using Git (New Pages and Images)"},{"location":"aerial_autonomy_stacks/","text":"Aerial Autonomy Stacks Based on this discussion in Discourse and our startup meeting , we can define an aerial autonomy stack as follows: An aerial autonomy robotics stack is a collection of building blocks that enable the development of autonomous aerial vehicles, by providing a modular and scalable architecture for sensing, perception, planning, and control tasks. It allows unmanned aerial vehicles to perform complex missions without human intervention, while accommodating different hardware configurations and simulation environments. Comparison From the paper Fernandez-Cortizas, Miguel, et al. \"Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems.\" arXiv preprint arXiv:2303.18237 (2023). the following autonomy stack table was extracted and adapted. Flight stack OS/OC Modular Tested in Middleware last update MF RO MA MP PO Aerostack \u2713 \u2713 S,RIL,ROL ROS 10/2021 \u2717 \u2713 \u2713 \u2713 \u2717 Aerostack2 \u2713 \u2713 S,RIL,ROL ROS 2 03/2023 \u2713 \u2713 \u2713 \u2713 \u2713 AerialCore \u2713 \u2713 S,RIL,ROL ROS 03/2023 \u2713 \u2713 \u2713 \u2717 \u2713 Agilicious \u2713 \u2713 S,RIL ROS 03/2023 \u2717 \u2713 \u2717 \u2717 \u2717 KumarRobotics \u2713 \u2717 S,RIL,ROL ROS 12/2022 \u2717 \u2713 \u2717 \u2713 \u2717 CrazyChoir \u2713 \u2717 S,RIL ROS 2 02/2023 \u2717 \u2713 \u2713 \u2717 \u2717 UAL \u2713 \u2717 S,RIL,ROL ROS 12/2022 \u2713 \u2717 \u2717 \u2713 \u2717 XTDrone \u2713 \u2713 S ROS 03/2023 \u2717 \u2713 \u2717 \u2717 \u2717 RotorS \u2713 \u2713 S ROS 07/2021 \u2717 \u2713 \u2717 \u2717 \u2717 GAAS \u2713 \u2713 S ROS 10/2021 \u2717 \u2717 \u2717 \u2717 \u2717 MRS AUV System \u2713 \u2713 S,RIL,ROL ROS 09/2023 \u2713 \u2713 \u2713 \u2713 \u2717 Crazyswarm \u2713 \u2717 S,RIL ROS 12/2022 \u2717 \u2713 \u2713 \u2717 \u2717 Crazyswarm2 \u2713 \u2713 S,RIL ROS 2 09/2023 \u2717 \u2713 \u2713 \u2717 \u2713 Abbrivations * OS/OC: Open source or Open code * S: Experiments in simulation * RIL: Experiments in the lab * ROL: Experiments outside the lab * MF: Multi-frame * RO: Rate output * MA: Multi agent * MP: Multi platform * PO: Plugin oriented VIO packages Visual Inertial Odometry packages is an very important strategy of positioning within GPS deprived environments. Since UAVs can not use wheel odometry and heavily relient on cameras, this is one of the main drivers for autonomous exploration with these vehicles. Here is a list of VIO packages that people can use if they have a depth camera on their platform. OpenVins (ROS1/ROS2): VINS-Fusion stand alone SLAMcore stand alone SDK ORB-SLAM3 ROS2 Visual Odometry Datasets Kaggle Zurich Urban Micro Aerial Vehicle Kaggle Underwater forward-looking VI dataset The Air Lab Datasets VICON ROS2 bag file Google drive The UZH FPV Dataset Working list autonomy stacks This is just a list of autonomy stacks with links, such that later we can add them to the overview. Working list: Aerostack2 Aerostack(1) KumarRobotics Autonomy Stack Agilicious Crazyswarm2 Crazyswarm(1) MRS UAV System Hector quadrotor RotorS RISE paper MRS AUV System Clover /HKUST-Aerial-Robotics Partial autonomy packages A list of packages which don't comprise a full stack but do offer value on top of basic flight controller firmware. MAVROS Controllers Indoor navigation packages Given the above Aerial Autonomy Stacks , the list below outlines specific implementations of indoor navigation software packages in ROS, running on aerial vehicle platforms. The list, though not exhaustive, provides a good overview of available off-the-shelf non-commercial software. Package name OS/OC Sensors required Middleware Simulator Platform/controller Last updated Ardupilot ROS \u2713 LiDAR ROS 2 Gazebo Iris coptor,Ardupilot 02/2024 as2_behaviour_tree \u2713 Unknown ROS 2 Gazebo Crazyflie,DJI,Tello 02/2024 Teach-Repeat-Replan \u2713 Stereo camera ROS 1 MockaFly DJI N3 11/2020 rtabmap \u2713 Stereo camera ROS 1 Gazebo PX4 05/2023 ORB_SLAM_3 \u2713 Mono/stereo camera ROS 1 N/A Bebop 2 06/2023 relative_nav \u2717 Stereo camera ROS 1 N/A Rotorcraft 04/2017 zephyr \u2713 LiDAR ROS 1 RotorS/Gazebo AscTec Firefly 11/2018 tum_ardrone \u2713 Mono camera ROS 1 N/A AR.Drone 05/2014 kr_autonomous_flight \u2713 Stereo camera/LiDAR/IMU ROS 1 Gazebo Pixhawk 08/2023 px4_sim_ros2 \u2713 Stereo camera ROS 2 Gazebo PX4 04/2024","title":"Aerial Autonomy Stacks"},{"location":"aerial_autonomy_stacks/#aerial-autonomy-stacks","text":"Based on this discussion in Discourse and our startup meeting , we can define an aerial autonomy stack as follows: An aerial autonomy robotics stack is a collection of building blocks that enable the development of autonomous aerial vehicles, by providing a modular and scalable architecture for sensing, perception, planning, and control tasks. It allows unmanned aerial vehicles to perform complex missions without human intervention, while accommodating different hardware configurations and simulation environments.","title":"Aerial Autonomy Stacks"},{"location":"aerial_autonomy_stacks/#comparison","text":"From the paper Fernandez-Cortizas, Miguel, et al. \"Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems.\" arXiv preprint arXiv:2303.18237 (2023). the following autonomy stack table was extracted and adapted. Flight stack OS/OC Modular Tested in Middleware last update MF RO MA MP PO Aerostack \u2713 \u2713 S,RIL,ROL ROS 10/2021 \u2717 \u2713 \u2713 \u2713 \u2717 Aerostack2 \u2713 \u2713 S,RIL,ROL ROS 2 03/2023 \u2713 \u2713 \u2713 \u2713 \u2713 AerialCore \u2713 \u2713 S,RIL,ROL ROS 03/2023 \u2713 \u2713 \u2713 \u2717 \u2713 Agilicious \u2713 \u2713 S,RIL ROS 03/2023 \u2717 \u2713 \u2717 \u2717 \u2717 KumarRobotics \u2713 \u2717 S,RIL,ROL ROS 12/2022 \u2717 \u2713 \u2717 \u2713 \u2717 CrazyChoir \u2713 \u2717 S,RIL ROS 2 02/2023 \u2717 \u2713 \u2713 \u2717 \u2717 UAL \u2713 \u2717 S,RIL,ROL ROS 12/2022 \u2713 \u2717 \u2717 \u2713 \u2717 XTDrone \u2713 \u2713 S ROS 03/2023 \u2717 \u2713 \u2717 \u2717 \u2717 RotorS \u2713 \u2713 S ROS 07/2021 \u2717 \u2713 \u2717 \u2717 \u2717 GAAS \u2713 \u2713 S ROS 10/2021 \u2717 \u2717 \u2717 \u2717 \u2717 MRS AUV System \u2713 \u2713 S,RIL,ROL ROS 09/2023 \u2713 \u2713 \u2713 \u2713 \u2717 Crazyswarm \u2713 \u2717 S,RIL ROS 12/2022 \u2717 \u2713 \u2713 \u2717 \u2717 Crazyswarm2 \u2713 \u2713 S,RIL ROS 2 09/2023 \u2717 \u2713 \u2713 \u2717 \u2713 Abbrivations * OS/OC: Open source or Open code * S: Experiments in simulation * RIL: Experiments in the lab * ROL: Experiments outside the lab * MF: Multi-frame * RO: Rate output * MA: Multi agent * MP: Multi platform * PO: Plugin oriented","title":"Comparison"},{"location":"aerial_autonomy_stacks/#vio-packages","text":"Visual Inertial Odometry packages is an very important strategy of positioning within GPS deprived environments. Since UAVs can not use wheel odometry and heavily relient on cameras, this is one of the main drivers for autonomous exploration with these vehicles. Here is a list of VIO packages that people can use if they have a depth camera on their platform. OpenVins (ROS1/ROS2): VINS-Fusion stand alone SLAMcore stand alone SDK ORB-SLAM3 ROS2","title":"VIO packages"},{"location":"aerial_autonomy_stacks/#visual-odometry-datasets","text":"Kaggle Zurich Urban Micro Aerial Vehicle Kaggle Underwater forward-looking VI dataset The Air Lab Datasets VICON ROS2 bag file Google drive The UZH FPV Dataset","title":"Visual Odometry Datasets"},{"location":"aerial_autonomy_stacks/#working-list-autonomy-stacks","text":"This is just a list of autonomy stacks with links, such that later we can add them to the overview. Working list: Aerostack2 Aerostack(1) KumarRobotics Autonomy Stack Agilicious Crazyswarm2 Crazyswarm(1) MRS UAV System Hector quadrotor RotorS RISE paper MRS AUV System Clover /HKUST-Aerial-Robotics","title":"Working list autonomy stacks"},{"location":"aerial_autonomy_stacks/#partial-autonomy-packages","text":"A list of packages which don't comprise a full stack but do offer value on top of basic flight controller firmware. MAVROS Controllers","title":"Partial autonomy packages"},{"location":"aerial_autonomy_stacks/#indoor-navigation-packages","text":"Given the above Aerial Autonomy Stacks , the list below outlines specific implementations of indoor navigation software packages in ROS, running on aerial vehicle platforms. The list, though not exhaustive, provides a good overview of available off-the-shelf non-commercial software. Package name OS/OC Sensors required Middleware Simulator Platform/controller Last updated Ardupilot ROS \u2713 LiDAR ROS 2 Gazebo Iris coptor,Ardupilot 02/2024 as2_behaviour_tree \u2713 Unknown ROS 2 Gazebo Crazyflie,DJI,Tello 02/2024 Teach-Repeat-Replan \u2713 Stereo camera ROS 1 MockaFly DJI N3 11/2020 rtabmap \u2713 Stereo camera ROS 1 Gazebo PX4 05/2023 ORB_SLAM_3 \u2713 Mono/stereo camera ROS 1 N/A Bebop 2 06/2023 relative_nav \u2717 Stereo camera ROS 1 N/A Rotorcraft 04/2017 zephyr \u2713 LiDAR ROS 1 RotorS/Gazebo AscTec Firefly 11/2018 tum_ardrone \u2713 Mono camera ROS 1 N/A AR.Drone 05/2014 kr_autonomous_flight \u2713 Stereo camera/LiDAR/IMU ROS 1 Gazebo Pixhawk 08/2023 px4_sim_ros2 \u2713 Stereo camera ROS 2 Gazebo PX4 04/2024","title":"Indoor navigation packages"},{"location":"aerial_vehicles/","text":"Aerial Vehicle Types and Categories Usually, people tend to think about quadcopters when talking about aerial robotics, but there is actually a much larger variety of vehicle types available! Here's an attempt to list all of them if we can. Copters Copters are aerial vehicles that use motors and propellers for their lift and maneuverability in 3D space. These usually are categorized into the following subcategories: Quadcopters: The most common aerial vehicle used. It is driven by (as the name already indicates) 4 propellers and 4 motors. Helicopters: Like manned helicopters, unmanned helicopters are driven by one main rotor on top and one smaller one on the side for stability and maneuverability. Hexacopters/Octocopters: Copters with 6 or 8 rotors/motor combinations, respectively. Tilt-Rotor Copters: Copters that have motor and rotor combos that can change orientation. Monocopters: Monocopters are copters that are only driven by a single rotor and motor combo. Fixed Wing Fixed-wing aerial vehicles use fixed wings for takeoff lift and control. These are the subcategories: Regular Fixed Wings: Like regular airplanes, fixed-wing UAVs have 2 wings with a form of propulsion. Hybrid / VTOL: These vehicles can take off like a quadcopter vertically but can transition and act like a fixed-wing vehicle for the remainder of the flight. Gliding UAVs: These are fixed-wing UAVs without any propulsion and are therefore reliant on wind and thermal currents. Flapping Wing Flapping-wing aerial vehicles generate both lift and thrust by their flapping wings. There are two main subcategories: Ornithopters: These are the traditional bird-like flapping-wing UAVs. The flapping wings generate lift and thrust and an airplane-like tail with traditional control surfaces is used for stbility and flight control. Some minimal forward velocity needs to be maintained such the wings produce sufficient lift. Only some smaller vehicles demonstrate limited VTOL and hovering capability. These vehicles can be passively stabilized by the tail surfaces and do not require active stabilization, similar to fixed-wing UAVs. Tail-less flapping-wing UAVs: These vehicles fly like insects and hummingbirds - they can hover, take-off and land vertically, and fly in any direction (forward & backward, sideways, up & down). Lift/thrust is generated by the flapping wings, whose geometry and/or motion is adjustable for flight control. Like multicopters, these vehicles require active stabilization, typically by using an onboard IMU. Blimps Blimps or airships can also be used as aerial vehicles as well. These vehicles stay buoyant by using a balloon with lighter-than-air gas. Maneuverability is either done with flapping wings or rotors. List to support vehicles per autopilot suite Each autopilot suite have their a wide range of supported vehicles, to which this itemized list is referenced to. Note that these lists also include non-aerial vehicles as well. Ardupilot supported vehicle types Paparazzi PX4 Crazyflie","title":"Aerial Vehicle Types and Categories"},{"location":"aerial_vehicles/#aerial-vehicle-types-and-categories","text":"Usually, people tend to think about quadcopters when talking about aerial robotics, but there is actually a much larger variety of vehicle types available! Here's an attempt to list all of them if we can.","title":"Aerial Vehicle Types and Categories"},{"location":"aerial_vehicles/#copters","text":"Copters are aerial vehicles that use motors and propellers for their lift and maneuverability in 3D space. These usually are categorized into the following subcategories: Quadcopters: The most common aerial vehicle used. It is driven by (as the name already indicates) 4 propellers and 4 motors. Helicopters: Like manned helicopters, unmanned helicopters are driven by one main rotor on top and one smaller one on the side for stability and maneuverability. Hexacopters/Octocopters: Copters with 6 or 8 rotors/motor combinations, respectively. Tilt-Rotor Copters: Copters that have motor and rotor combos that can change orientation. Monocopters: Monocopters are copters that are only driven by a single rotor and motor combo.","title":"Copters"},{"location":"aerial_vehicles/#fixed-wing","text":"Fixed-wing aerial vehicles use fixed wings for takeoff lift and control. These are the subcategories: Regular Fixed Wings: Like regular airplanes, fixed-wing UAVs have 2 wings with a form of propulsion. Hybrid / VTOL: These vehicles can take off like a quadcopter vertically but can transition and act like a fixed-wing vehicle for the remainder of the flight. Gliding UAVs: These are fixed-wing UAVs without any propulsion and are therefore reliant on wind and thermal currents.","title":"Fixed Wing"},{"location":"aerial_vehicles/#flapping-wing","text":"Flapping-wing aerial vehicles generate both lift and thrust by their flapping wings. There are two main subcategories: Ornithopters: These are the traditional bird-like flapping-wing UAVs. The flapping wings generate lift and thrust and an airplane-like tail with traditional control surfaces is used for stbility and flight control. Some minimal forward velocity needs to be maintained such the wings produce sufficient lift. Only some smaller vehicles demonstrate limited VTOL and hovering capability. These vehicles can be passively stabilized by the tail surfaces and do not require active stabilization, similar to fixed-wing UAVs. Tail-less flapping-wing UAVs: These vehicles fly like insects and hummingbirds - they can hover, take-off and land vertically, and fly in any direction (forward & backward, sideways, up & down). Lift/thrust is generated by the flapping wings, whose geometry and/or motion is adjustable for flight control. Like multicopters, these vehicles require active stabilization, typically by using an onboard IMU.","title":"Flapping Wing"},{"location":"aerial_vehicles/#blimps","text":"Blimps or airships can also be used as aerial vehicles as well. These vehicles stay buoyant by using a balloon with lighter-than-air gas. Maneuverability is either done with flapping wings or rotors.","title":"Blimps"},{"location":"aerial_vehicles/#list-to-support-vehicles-per-autopilot-suite","text":"Each autopilot suite have their a wide range of supported vehicles, to which this itemized list is referenced to. Note that these lists also include non-aerial vehicles as well. Ardupilot supported vehicle types Paparazzi PX4 Crazyflie","title":"List to support vehicles per autopilot suite"},{"location":"autopilots_suites/","text":"Autopilot Suites There are several autopilot suites available for control-boards for aerial vehicles. This page mostly have an overview of all of those including some comparison factors in them as well. Autopilot suite startup year latest version OS licence ROS support supported vehicles Ardupilot ( GitHub ) 2009 4.5.2 (05/24) GPL 3.0 yes Copters, Fixed wings, VTOL Betaflight ( GitHub ) 2015 4.5.0 (04/24) GPL 3.0 no Copters crazyflie-firmware ( GitHub ) 2011 2024.2 GPL 3.0 yes* Quadcopters, Flapping wings DJI autopilot ( GitHub ) 2006 2023.9 closed yes Copters, VTOLS Paparazzi ( GitHub ) 2003 6.3.0 (12/23) GPL 2.0 no Copters, Fixed wings, VTOL PX4 ( GitHub ) 2009 1.14.0 (10/23) BSD 3-Clause yes Copters, Fixed wings, VTOL ROSflight ( GitHub ) 2019 2.2.0 beta (09/23) BSD 3-Clause yes Copters, fixed wings *community provided support","title":"Autopilot Suites"},{"location":"autopilots_suites/#autopilot-suites","text":"There are several autopilot suites available for control-boards for aerial vehicles. This page mostly have an overview of all of those including some comparison factors in them as well. Autopilot suite startup year latest version OS licence ROS support supported vehicles Ardupilot ( GitHub ) 2009 4.5.2 (05/24) GPL 3.0 yes Copters, Fixed wings, VTOL Betaflight ( GitHub ) 2015 4.5.0 (04/24) GPL 3.0 no Copters crazyflie-firmware ( GitHub ) 2011 2024.2 GPL 3.0 yes* Quadcopters, Flapping wings DJI autopilot ( GitHub ) 2006 2023.9 closed yes Copters, VTOLS Paparazzi ( GitHub ) 2003 6.3.0 (12/23) GPL 2.0 no Copters, Fixed wings, VTOL PX4 ( GitHub ) 2009 1.14.0 (10/23) BSD 3-Clause yes Copters, Fixed wings, VTOL ROSflight ( GitHub ) 2019 2.2.0 beta (09/23) BSD 3-Clause yes Copters, fixed wings *community provided support","title":"Autopilot Suites"},{"location":"education_and_tutorial/","text":"Education and tutorials This page will contain a list of education courses or tutorials for aerial robotics. This is a partly compilation of this ROS discourse thread and the May meeting about tutorials and education and some googling :) This also includes input from a Linkedin post for a call of suggestions. This has been an adaption from the following Bitcraze Blogpost , and the overview presentation done for the ROS-aerial CWG meetings Course explanation This section explains some of the recourses in more detail. Books There is this important resource which is the book titled \u2018Small Unmanned Aircraft: Theory and Practice.\u2019 This book has been written by Randy Beard and Tim McLain of Brigham Young University, and it covers everything from the absolute basics of coordinate frames and quadrotor dynamics to path planning and cameras. It is a must-read for anybody starting in UAVs and Aerial robotics. The physical book can be found here: http://press.princeton.edu/titles/9632.html The available PDFs can be accessed on GitHub: https://github.com/randybeard/uavbook Online Courses on Aerial Robotics This section shows online courses for aerial robotics with online instructor. Coursera offers the \u2018Robotics: Aerial Robotics\u2019 course as part of the Robotics specialization. Taught by Prof. Vijay Kumar from Penn University, this 4-week course covers the mechanics and control of aerial vehicles using Matlab. It starts from 1 dimension and gradually progresses to the 3rd dimension in simulation. The course is part of a paid educational program, but you can audit the lessons for free. Link: https://www.coursera.org/learn/robotics-flight Udacity has been offering a course on Aerial Vehicles for quite some time for the Flying car nano degree . The lessons are taught by top names in the industry and cover key aspects of Aerial Robotics, such as motion planning, controls, and estimation, with lab assignments involving a real drone. The course duration is 4 months, and access is available for a fee. Link: https://www.udacity.com/course/flying-car-nanodegree\u2013nd787 edX offers the 'ETHx: Autonomous Mobile Robots' course. Taught by Prof. Roland Siegwart, Dr. Davide Scaramuzza, Prof. Marco Hutter, Prof. Margarita Chli, Prof. Martin Rufli and Prof. Nicholas Lawrance from ETH Zurich, this 15-week course focuses on the principles of autonomous navigation and control of mobile robots. It covers topics such as perception, localization, planning, and control, which are essential for enabling robots to operate autonomously in dynamic environments. You can audit the course material for free with limited access. Link: https://www.edx.org/learn/autonomous-robotics/eth-zurich-autonomous-mobile-robots?utm_campaign=social-sharing-course-page&utm_medium=social&utm_source=email Additionally, there\u2019s the course \u2018Applied Control System 3: UAV Drone (3D Dynamics & Control)\u2019 which is part of a series by Mark Misin. This course delves deep into the dynamics, control, and modeling of quadrotors. Link: https://www.udemy.com/course/applied-control-systems-for-engineers-2-uav-drone-control/ University courses on Aerial Robotics with open resources This section showes university courses that have released recordings of lectures, slides and/or assignments. For instructions participants would need to follow the actual course at that one university. The University of Maryland offers a course on Autonomous Aerial Robotics , making all videos, slides, and assignments available. Taught by Nitin J. Sanket and Chahat Deep Singh, the course covers everything from basic control and dynamics to full autonomy. It\u2019s a comprehensive resource for aerial robotics. The course utilizes the Parrot Bebop 2.0 , and while a Mocap system is required, you may explore the possibility of adapting the course to a different platform. ROS is also part of this course Link: http://prg.cs.umd.edu/enae788m \u2018Visual Navigation For Autonomous Vehicles\u2019 is a course available on MIT Open Courseware, taught by Prof. Luca Carlone. As the name implies, the course primarily focuses on autonomous navigation for any autonomous vehicle. It includes exercises where students implement vision algorithms on both ground robots and drones. Additionally, the course covers working with ROS and applying the knowledge to a simulated drone in Unity. The students also get to learn how to work with ROS Link: https://ocw.mit.edu/courses/16-485-visual-navigation-for-autonomous-vehicles-vnav-fall-2020/ The \u2018Bio-inspired Robotics\u2019 course at the University of Washington, led by Prof. Sawyer Fuller, explores the realm of drawing inspiration from nature rather than reinventing the wheel. It covers various robots inspired by creatures capable of swimming, walking, hopping, and of course, flying. Lab assignments in this course involve working with a Crazyflie drone. Link: https://faculty.washington.edu/minster/bio_inspired_robotics/ Brown University offers a course called \u2018Introduction to Robotics\u2019 taught by Prof. Stefanie Tellix. While the introduction covers generic robotics, the focus of the full course is on building and programming the Duckiedrone . The course dives straight into autonomy and also teaches students how to work with ROS . Link: https://cs.brown.edu/courses/cs1951r/ Princeton University have also decided to release their \u2018Intro to Robotics\u2019 lectures and materials for the public. It covers all from control and estimation, computer vision and planning. Also it offers lab assignments with the Crazyflie . Link: https://irom-lab.princeton.edu/intro-to-robotics/ Youtube Youtube also has quite some tutorials available so this section highlights a few. Drone Programming with Python : This popular tutorial/course teaches viewers how to program a real drone using Python with the DJI Tello . It offers a great opportunity for anyone looking for a short and enjoyable project to undertake, especially on a rainy day, while still working with a real platform. Link: https://youtu.be/LmEcyQnfpDA Intelligent Quads YouTube Channel : This channel is entirely dedicated to creating autonomous UAVs, covering topics from Ardupilot to MAVlink to ROS and Gazebo. It appears to be a valuable resource for beginners in the field of autonomous UAVs. This also includes ROS as part of the lessons as well. Link: https://www.youtube.com/@IntelligentQuads Code Examples Here are some code examples that can be used as reference for experiments. Mambo ROS Examples: This is a collection of experiments targeted Parrot Mambo drones, there are experiments with one or multiple vehicles at the same time. It runs over ROS over BLE, and some test are adapted to make use of a Vicon MoCap system. From a control theory's perspective, it showcases an optional robust control strategy, using an H-Infinity controller with perturbation estimation and a identified dynamic model of a Parrot Mambo drone. Link: https://github.com/TOTON95/Mambo_ROS_Examples Some special mentions So here there are some courses that either doesn't fit in the above categories or are deprecated. University of Twente UAV Centre: The University of Twente has created a portal with a variety of UAV-related courses. You can find a wealth of information and educational materials on their website. Link: https://www.itc.nl/facilities/centres-of-expertise/uav-centre/ Self-Driving Car Specialization: If you are interested in learning more about SLAM (Simultaneous Localization and Mapping) and sensors, this specialization is tailored for self-driving cars but the theory can be useful for drones as well. Link: https://www.coursera.org/specializations/self-driving-cars Autonomous Navigation for Flying Robots: This older course is still highly relevant for anyone interested in autonomous navigation for flying robots. It offers valuable insights and knowledge. Link: https://www.edx.org/course/autonomous-navigation-for-flying-robots Drone Dojo: For those looking to build their own drones, Drone Dojo provides useful instructions and courses to get started on DIY drone projects. Link: https://dojofordrones.com/ Bachelor Majors in UAV Engineering: If you are fully committed to pursuing a career in aerial robotics, both Embry-Riddle Aeronautical University and the University of North Dakota offer full bachelor\u2019s majors in becoming a UAV engineer. Embry-Riddle Aeronautical University: https://erau.edu/degrees/bachelor/unmanned-aircraft-systems University of North Dakota: https://und.edu/programs/unmanned-aircraft-system-operations-bs-aero/ Working list This list contains some resources that we haven't included in the overview. Remove the item once it has been included PX4 getting started page Learning ArduPilot Aerial robotics 101 medium article Aerial robotics with ROS (work in progress) Bitcraze crazyflie tutorial page Simnet + Ardupilot academy Autonomy course Worcester Polytechnic Institute List of robotic aerial resources Credit Lots of thanks for anybody contributing to this linkedin post . This was extremely helpful!","title":"Education and tutorials"},{"location":"education_and_tutorial/#education-and-tutorials","text":"This page will contain a list of education courses or tutorials for aerial robotics. This is a partly compilation of this ROS discourse thread and the May meeting about tutorials and education and some googling :) This also includes input from a Linkedin post for a call of suggestions. This has been an adaption from the following Bitcraze Blogpost , and the overview presentation done for the ROS-aerial CWG meetings","title":"Education and tutorials"},{"location":"education_and_tutorial/#course-explanation","text":"This section explains some of the recourses in more detail.","title":"Course explanation"},{"location":"education_and_tutorial/#books","text":"There is this important resource which is the book titled \u2018Small Unmanned Aircraft: Theory and Practice.\u2019 This book has been written by Randy Beard and Tim McLain of Brigham Young University, and it covers everything from the absolute basics of coordinate frames and quadrotor dynamics to path planning and cameras. It is a must-read for anybody starting in UAVs and Aerial robotics. The physical book can be found here: http://press.princeton.edu/titles/9632.html The available PDFs can be accessed on GitHub: https://github.com/randybeard/uavbook","title":"Books"},{"location":"education_and_tutorial/#online-courses-on-aerial-robotics","text":"This section shows online courses for aerial robotics with online instructor. Coursera offers the \u2018Robotics: Aerial Robotics\u2019 course as part of the Robotics specialization. Taught by Prof. Vijay Kumar from Penn University, this 4-week course covers the mechanics and control of aerial vehicles using Matlab. It starts from 1 dimension and gradually progresses to the 3rd dimension in simulation. The course is part of a paid educational program, but you can audit the lessons for free. Link: https://www.coursera.org/learn/robotics-flight Udacity has been offering a course on Aerial Vehicles for quite some time for the Flying car nano degree . The lessons are taught by top names in the industry and cover key aspects of Aerial Robotics, such as motion planning, controls, and estimation, with lab assignments involving a real drone. The course duration is 4 months, and access is available for a fee. Link: https://www.udacity.com/course/flying-car-nanodegree\u2013nd787 edX offers the 'ETHx: Autonomous Mobile Robots' course. Taught by Prof. Roland Siegwart, Dr. Davide Scaramuzza, Prof. Marco Hutter, Prof. Margarita Chli, Prof. Martin Rufli and Prof. Nicholas Lawrance from ETH Zurich, this 15-week course focuses on the principles of autonomous navigation and control of mobile robots. It covers topics such as perception, localization, planning, and control, which are essential for enabling robots to operate autonomously in dynamic environments. You can audit the course material for free with limited access. Link: https://www.edx.org/learn/autonomous-robotics/eth-zurich-autonomous-mobile-robots?utm_campaign=social-sharing-course-page&utm_medium=social&utm_source=email Additionally, there\u2019s the course \u2018Applied Control System 3: UAV Drone (3D Dynamics & Control)\u2019 which is part of a series by Mark Misin. This course delves deep into the dynamics, control, and modeling of quadrotors. Link: https://www.udemy.com/course/applied-control-systems-for-engineers-2-uav-drone-control/","title":"Online Courses on Aerial Robotics"},{"location":"education_and_tutorial/#university-courses-on-aerial-robotics-with-open-resources","text":"This section showes university courses that have released recordings of lectures, slides and/or assignments. For instructions participants would need to follow the actual course at that one university. The University of Maryland offers a course on Autonomous Aerial Robotics , making all videos, slides, and assignments available. Taught by Nitin J. Sanket and Chahat Deep Singh, the course covers everything from basic control and dynamics to full autonomy. It\u2019s a comprehensive resource for aerial robotics. The course utilizes the Parrot Bebop 2.0 , and while a Mocap system is required, you may explore the possibility of adapting the course to a different platform. ROS is also part of this course Link: http://prg.cs.umd.edu/enae788m \u2018Visual Navigation For Autonomous Vehicles\u2019 is a course available on MIT Open Courseware, taught by Prof. Luca Carlone. As the name implies, the course primarily focuses on autonomous navigation for any autonomous vehicle. It includes exercises where students implement vision algorithms on both ground robots and drones. Additionally, the course covers working with ROS and applying the knowledge to a simulated drone in Unity. The students also get to learn how to work with ROS Link: https://ocw.mit.edu/courses/16-485-visual-navigation-for-autonomous-vehicles-vnav-fall-2020/ The \u2018Bio-inspired Robotics\u2019 course at the University of Washington, led by Prof. Sawyer Fuller, explores the realm of drawing inspiration from nature rather than reinventing the wheel. It covers various robots inspired by creatures capable of swimming, walking, hopping, and of course, flying. Lab assignments in this course involve working with a Crazyflie drone. Link: https://faculty.washington.edu/minster/bio_inspired_robotics/ Brown University offers a course called \u2018Introduction to Robotics\u2019 taught by Prof. Stefanie Tellix. While the introduction covers generic robotics, the focus of the full course is on building and programming the Duckiedrone . The course dives straight into autonomy and also teaches students how to work with ROS . Link: https://cs.brown.edu/courses/cs1951r/ Princeton University have also decided to release their \u2018Intro to Robotics\u2019 lectures and materials for the public. It covers all from control and estimation, computer vision and planning. Also it offers lab assignments with the Crazyflie . Link: https://irom-lab.princeton.edu/intro-to-robotics/","title":"University courses on Aerial Robotics with open resources"},{"location":"education_and_tutorial/#youtube","text":"Youtube also has quite some tutorials available so this section highlights a few. Drone Programming with Python : This popular tutorial/course teaches viewers how to program a real drone using Python with the DJI Tello . It offers a great opportunity for anyone looking for a short and enjoyable project to undertake, especially on a rainy day, while still working with a real platform. Link: https://youtu.be/LmEcyQnfpDA Intelligent Quads YouTube Channel : This channel is entirely dedicated to creating autonomous UAVs, covering topics from Ardupilot to MAVlink to ROS and Gazebo. It appears to be a valuable resource for beginners in the field of autonomous UAVs. This also includes ROS as part of the lessons as well. Link: https://www.youtube.com/@IntelligentQuads","title":"Youtube"},{"location":"education_and_tutorial/#code-examples","text":"Here are some code examples that can be used as reference for experiments. Mambo ROS Examples: This is a collection of experiments targeted Parrot Mambo drones, there are experiments with one or multiple vehicles at the same time. It runs over ROS over BLE, and some test are adapted to make use of a Vicon MoCap system. From a control theory's perspective, it showcases an optional robust control strategy, using an H-Infinity controller with perturbation estimation and a identified dynamic model of a Parrot Mambo drone. Link: https://github.com/TOTON95/Mambo_ROS_Examples","title":"Code Examples"},{"location":"education_and_tutorial/#some-special-mentions","text":"So here there are some courses that either doesn't fit in the above categories or are deprecated. University of Twente UAV Centre: The University of Twente has created a portal with a variety of UAV-related courses. You can find a wealth of information and educational materials on their website. Link: https://www.itc.nl/facilities/centres-of-expertise/uav-centre/ Self-Driving Car Specialization: If you are interested in learning more about SLAM (Simultaneous Localization and Mapping) and sensors, this specialization is tailored for self-driving cars but the theory can be useful for drones as well. Link: https://www.coursera.org/specializations/self-driving-cars Autonomous Navigation for Flying Robots: This older course is still highly relevant for anyone interested in autonomous navigation for flying robots. It offers valuable insights and knowledge. Link: https://www.edx.org/course/autonomous-navigation-for-flying-robots Drone Dojo: For those looking to build their own drones, Drone Dojo provides useful instructions and courses to get started on DIY drone projects. Link: https://dojofordrones.com/ Bachelor Majors in UAV Engineering: If you are fully committed to pursuing a career in aerial robotics, both Embry-Riddle Aeronautical University and the University of North Dakota offer full bachelor\u2019s majors in becoming a UAV engineer. Embry-Riddle Aeronautical University: https://erau.edu/degrees/bachelor/unmanned-aircraft-systems University of North Dakota: https://und.edu/programs/unmanned-aircraft-system-operations-bs-aero/","title":"Some special mentions"},{"location":"education_and_tutorial/#working-list","text":"This list contains some resources that we haven't included in the overview. Remove the item once it has been included PX4 getting started page Learning ArduPilot Aerial robotics 101 medium article Aerial robotics with ROS (work in progress) Bitcraze crazyflie tutorial page Simnet + Ardupilot academy Autonomy course Worcester Polytechnic Institute List of robotic aerial resources","title":"Working list"},{"location":"education_and_tutorial/#credit","text":"Lots of thanks for anybody contributing to this linkedin post . This was extremely helpful!","title":"Credit"},{"location":"hardware/","text":"Hardware, Components, and Dev Kits This is an list of development platforms for aerial robotics. Please start a pull request if you'd like to update these. Standard Commercial Research Platforms These are platforms that are currently commercially available for anybody to buy for their research. Copters Holybro: Holybro X500 V2 - PX4 Developer Kit PX4 Vision Dev Kit V1.5 ModalAI: PX4 Autonomy Dev Kit Starling 2 and Starling 2 Max Crazyflie 2.1 - Bitcraze NXP HoverGames Kit official hardware for the yearly HoverGames Challenge Duckietown: Duckiedrone DD21 Duckiedrone DD24 Clover by Coex PX4 Autonomy Developer Kit by ModelAI Droneblocks DEXI 5 3DR Quad Zero Kit Flapping wing Flapper Nimble+ insect-inspired UAV by Flapper Drones Industrial Platforms Uvify IFO-S Tricopter voliro AG DJI M300 Inhouse-developed platforms These are platforms that are standard within a lab or department, with information of what it contains provided with perhaps build instructions., Agilicous - University of Zurich ModQuad - Lehigh University RMF-Owl - Norwegian University of Science and Technology MiniHawk-VTOL Discontinued Platforms DJI M100 DJI tello Components Many of the UAVs are usually built by hand and composed of different components. This usually consists of a drone frame, flight controller boards, companion computers and of course motors, batteries and ESCs. Drone Frames Many drone frames are usually built from carbon fiber and custom-made for application or research. There are some frames that are provided that will provide some base: - DJI Flame wheel ARF kit F550, F450, F330 - Momentum Drones DEV-7 Flight controllers Holybro Pixhawk 4 Holybro Pixhawk 6C Holybro Pixhawk 6X Holybro Pixhawk 6X PRO CUAV's Pixhawk V6x mRobotics/3DR mRo PixRacer R15 discontinued, go look at: mRo PixRacerPro 3DR Control Zero Classic 3DR Control Zero H7 OEM 3DR Reference Design Carrier Board 3DR \"Stick\" Adapter (Carrier Board) Crazyflie Bolt 1.1 ARK Electronics ARK Electronics ARKV6X ARK Electronics Pi6X Flow Companion Computers For the drones that can carry it, the companion computers are important since they can do additional computations that the flight controller can not easily do. As these are capable of running some form of Linux, these can handle for instance computer vision with OpenCV or run nodes with ROS . Some companion computers also integrate flight control (RTOS) hardware in the same package Nvidia Jetson Xavier or the TX2 Module Nvidia Jetson Orin Intel Nuc Boards Raspberry PI 3 (A+) or Raspberry Pi 4 Khadas Vim3 Odroid (various boards) NXP NAVQPlus Qualcomm RB5 MRD5165 Eagle Kit (coming soon) VOXL 2 by Model AI EchoPilot AI AMD Xilinx Kria Starter Kits LattePanda x86 boards Carrier boards Several vendors have developed carrier boards that can expose input/output ports of companion computers mentioned above which are packaged in a System-on-Module (SoM) form factor and also offer a standard interface for plugging in popular flight controllers/their own FCs. Mistral MRD5165 Eagle based on Qualcomm RB5 ARK Electronics Jetson PAB carrier ARK Electronics Pi6X Flow Holybro Jetson Baseboard Dronee Lychee Drone autopilot hardware Airvolute DroneCore2 Jetson + Cube Depth Cameras Intel RealSense T265 Discontinued, look at: RealSense D455 RealSense D435i Oak-d Series like OAK-D Pro or OAK-D-Lite VOXL CAM by ModelAI Monocular Cameras Raspberry PI Cameras Compatible with RPI. IMX219 and IMX477 based cameras compatible with Jetson Xavier, NX, Nano, Orin series (other models may require device tree overlays or source changes). Google Coral Camera Compatible with NXP NAVQPLUS TOF Cameras Liteon A65 Camera Starter Kit Check with vendor for NXP NAVQPLUS compatibility. PMD Flexx2 3D Camera USB version should be compatible with most companion computers. VOXL2 TOF Depth Sensor Compatible with VOXL2. Check with vendor for early access product. Reference Bill of Materials Holybro S500v2 The Holybro S500v2 is a popular, relatively low-cost quadcopter. This is a list of the parts used with details of battery, motors, ESCs, and propellers with reference links to guide custom builds. S.No Part Name Part category Description Price (USD) Qty Total Cost (USD) Official/Reference Link 1 Holybro S500 frame Frame With landing gear, 385x385mm 42 1 42 https://holybro.com/collections/s500/products/s500-v2-kit?variant=42724497391805 2 Holybro Pixhawk 6C + GPS + Power module FC + GPS + Power module PM02 power module, M9N GPS 290 1 290 https://holybro.com/products/pixhawk-6c?variant=43005243785405 3 Holybro 2216 920KV CW Motor 19x19 mounting clockwise rotation 20 2 40 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094608061 4 Holybro 2216 920KV CCW Motor 19x19 mounting counter-clockwise rotation 20 2 40 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094640829 5 BLHeli S 20A ESC ESC Electronic Speed Controller to drive motors 14 4 56 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094706365 6 1045 propellers Props 10x4.5\" kit of 2 pairs 12 1 12 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094313149 7 Radiomaster R81 receiver Radio receiver Used for manually flying drone Line of Sight/testing* 18 1 18 https://holybro.com/products/radiomaster-r81-receiver 8 Holybro SiK telemetry radio v3 Telemetry link radio 500mW, 433MHz variant, pair of transmitter + receiver** 63 1 63 https://holybro.com/products/sik-telemetry-radio-v3?variant=42801817485501 9 Tattu 5200mAh 4S Battery 4S1P XT60 plug 35C 63 1 63 https://genstattu.com/tattu-5200mah-14-8v-35c-4s1p-lipo-battery-pack-with-xt60-plug.html Grand Total 624 Notes : *It can be used with Radiomaster Multiprotocol (4 in 1) or CC2500 based Radio Controller like FrSky Taranis X9D or similar **Used for connecting to ground control station, 915MHz variant also available Useful tool for this page : https://tabletomarkdown.com/convert-spreadsheet-to-markdown/","title":"Hardware, Components, and Dev Kits"},{"location":"hardware/#hardware-components-and-dev-kits","text":"This is an list of development platforms for aerial robotics. Please start a pull request if you'd like to update these.","title":"Hardware, Components, and Dev Kits"},{"location":"hardware/#standard-commercial-research-platforms","text":"These are platforms that are currently commercially available for anybody to buy for their research.","title":"Standard Commercial Research Platforms"},{"location":"hardware/#copters","text":"Holybro: Holybro X500 V2 - PX4 Developer Kit PX4 Vision Dev Kit V1.5 ModalAI: PX4 Autonomy Dev Kit Starling 2 and Starling 2 Max Crazyflie 2.1 - Bitcraze NXP HoverGames Kit official hardware for the yearly HoverGames Challenge Duckietown: Duckiedrone DD21 Duckiedrone DD24 Clover by Coex PX4 Autonomy Developer Kit by ModelAI Droneblocks DEXI 5 3DR Quad Zero Kit","title":"Copters"},{"location":"hardware/#flapping-wing","text":"Flapper Nimble+ insect-inspired UAV by Flapper Drones","title":"Flapping wing"},{"location":"hardware/#industrial-platforms","text":"Uvify IFO-S Tricopter voliro AG DJI M300","title":"Industrial Platforms"},{"location":"hardware/#inhouse-developed-platforms","text":"These are platforms that are standard within a lab or department, with information of what it contains provided with perhaps build instructions., Agilicous - University of Zurich ModQuad - Lehigh University RMF-Owl - Norwegian University of Science and Technology MiniHawk-VTOL","title":"Inhouse-developed platforms"},{"location":"hardware/#discontinued-platforms","text":"DJI M100 DJI tello","title":"Discontinued Platforms"},{"location":"hardware/#components","text":"Many of the UAVs are usually built by hand and composed of different components. This usually consists of a drone frame, flight controller boards, companion computers and of course motors, batteries and ESCs.","title":"Components"},{"location":"hardware/#drone-frames","text":"Many drone frames are usually built from carbon fiber and custom-made for application or research. There are some frames that are provided that will provide some base: - DJI Flame wheel ARF kit F550, F450, F330 - Momentum Drones DEV-7","title":"Drone Frames"},{"location":"hardware/#flight-controllers","text":"Holybro Pixhawk 4 Holybro Pixhawk 6C Holybro Pixhawk 6X Holybro Pixhawk 6X PRO CUAV's Pixhawk V6x mRobotics/3DR mRo PixRacer R15 discontinued, go look at: mRo PixRacerPro 3DR Control Zero Classic 3DR Control Zero H7 OEM 3DR Reference Design Carrier Board 3DR \"Stick\" Adapter (Carrier Board) Crazyflie Bolt 1.1 ARK Electronics ARK Electronics ARKV6X ARK Electronics Pi6X Flow","title":"Flight controllers"},{"location":"hardware/#companion-computers","text":"For the drones that can carry it, the companion computers are important since they can do additional computations that the flight controller can not easily do. As these are capable of running some form of Linux, these can handle for instance computer vision with OpenCV or run nodes with ROS . Some companion computers also integrate flight control (RTOS) hardware in the same package Nvidia Jetson Xavier or the TX2 Module Nvidia Jetson Orin Intel Nuc Boards Raspberry PI 3 (A+) or Raspberry Pi 4 Khadas Vim3 Odroid (various boards) NXP NAVQPlus Qualcomm RB5 MRD5165 Eagle Kit (coming soon) VOXL 2 by Model AI EchoPilot AI AMD Xilinx Kria Starter Kits LattePanda x86 boards","title":"Companion Computers"},{"location":"hardware/#carrier-boards","text":"Several vendors have developed carrier boards that can expose input/output ports of companion computers mentioned above which are packaged in a System-on-Module (SoM) form factor and also offer a standard interface for plugging in popular flight controllers/their own FCs. Mistral MRD5165 Eagle based on Qualcomm RB5 ARK Electronics Jetson PAB carrier ARK Electronics Pi6X Flow Holybro Jetson Baseboard Dronee Lychee Drone autopilot hardware Airvolute DroneCore2 Jetson + Cube","title":"Carrier boards"},{"location":"hardware/#depth-cameras","text":"Intel RealSense T265 Discontinued, look at: RealSense D455 RealSense D435i Oak-d Series like OAK-D Pro or OAK-D-Lite VOXL CAM by ModelAI","title":"Depth Cameras"},{"location":"hardware/#monocular-cameras","text":"Raspberry PI Cameras Compatible with RPI. IMX219 and IMX477 based cameras compatible with Jetson Xavier, NX, Nano, Orin series (other models may require device tree overlays or source changes). Google Coral Camera Compatible with NXP NAVQPLUS","title":"Monocular Cameras"},{"location":"hardware/#tof-cameras","text":"Liteon A65 Camera Starter Kit Check with vendor for NXP NAVQPLUS compatibility. PMD Flexx2 3D Camera USB version should be compatible with most companion computers. VOXL2 TOF Depth Sensor Compatible with VOXL2. Check with vendor for early access product.","title":"TOF Cameras"},{"location":"hardware/#reference-bill-of-materials","text":"","title":"Reference Bill of Materials"},{"location":"hardware/#holybro-s500v2","text":"The Holybro S500v2 is a popular, relatively low-cost quadcopter. This is a list of the parts used with details of battery, motors, ESCs, and propellers with reference links to guide custom builds. S.No Part Name Part category Description Price (USD) Qty Total Cost (USD) Official/Reference Link 1 Holybro S500 frame Frame With landing gear, 385x385mm 42 1 42 https://holybro.com/collections/s500/products/s500-v2-kit?variant=42724497391805 2 Holybro Pixhawk 6C + GPS + Power module FC + GPS + Power module PM02 power module, M9N GPS 290 1 290 https://holybro.com/products/pixhawk-6c?variant=43005243785405 3 Holybro 2216 920KV CW Motor 19x19 mounting clockwise rotation 20 2 40 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094608061 4 Holybro 2216 920KV CCW Motor 19x19 mounting counter-clockwise rotation 20 2 40 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094640829 5 BLHeli S 20A ESC ESC Electronic Speed Controller to drive motors 14 4 56 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094706365 6 1045 propellers Props 10x4.5\" kit of 2 pairs 12 1 12 https://holybro.com/products/spare-parts-s500-v2-kit?variant=41591094313149 7 Radiomaster R81 receiver Radio receiver Used for manually flying drone Line of Sight/testing* 18 1 18 https://holybro.com/products/radiomaster-r81-receiver 8 Holybro SiK telemetry radio v3 Telemetry link radio 500mW, 433MHz variant, pair of transmitter + receiver** 63 1 63 https://holybro.com/products/sik-telemetry-radio-v3?variant=42801817485501 9 Tattu 5200mAh 4S Battery 4S1P XT60 plug 35C 63 1 63 https://genstattu.com/tattu-5200mah-14-8v-35c-4s1p-lipo-battery-pack-with-xt60-plug.html Grand Total 624 Notes : *It can be used with Radiomaster Multiprotocol (4 in 1) or CC2500 based Radio Controller like FrSky Taranis X9D or similar **Used for connecting to ground control station, 915MHz variant also available Useful tool for this page : https://tabletomarkdown.com/convert-spreadsheet-to-markdown/","title":"Holybro S500v2"},{"location":"message_standards/","text":"Message standards REP 147 A Standard interface for Aerial Vehicles Within the ROS ecoysystem, there is currently a ROS enhancement proposal (REP) available to give guidance to message standards for UAVS. This can be found here: REP 147 A Standard interface for Aerial Vehicles Message standards within autopilot suites MAVlink MAVLink, an acronym for Micro Air Vehicle Link, is a communication system mainly used for the exchange of information between unmanned aerial vehicles and ground control stations. This protocol, which was launched in 2009, is structured as a header-only message marshaling library. MAVLink is versatile, supporting a broad range of messages and capable of being sent over virtually any type of serial connection, including Wi-Fi and radio technologies. In terms of messages, they are defined at compile time in XML files, which are processed to create libraries for sending and receiving messages. If new messages are added or existing ones are modified, the XML definitions need to be updated and the application recompiled. https://mavlink.io/en/ MAVlink is used in 2 autopilot suites. Eventhough the same message type are being shared in both the suites, the accomendating behavior might differ: Ardupilot MAVlink usage PX4 MAVlink usage","title":"Message standards"},{"location":"message_standards/#message-standards","text":"","title":"Message standards"},{"location":"message_standards/#rep-147-a-standard-interface-for-aerial-vehicles","text":"Within the ROS ecoysystem, there is currently a ROS enhancement proposal (REP) available to give guidance to message standards for UAVS. This can be found here: REP 147 A Standard interface for Aerial Vehicles","title":"REP 147 A Standard interface for Aerial Vehicles"},{"location":"message_standards/#message-standards-within-autopilot-suites","text":"","title":"Message standards within autopilot suites"},{"location":"message_standards/#mavlink","text":"MAVLink, an acronym for Micro Air Vehicle Link, is a communication system mainly used for the exchange of information between unmanned aerial vehicles and ground control stations. This protocol, which was launched in 2009, is structured as a header-only message marshaling library. MAVLink is versatile, supporting a broad range of messages and capable of being sent over virtually any type of serial connection, including Wi-Fi and radio technologies. In terms of messages, they are defined at compile time in XML files, which are processed to create libraries for sending and receiving messages. If new messages are added or existing ones are modified, the XML definitions need to be updated and the application recompiled. https://mavlink.io/en/ MAVlink is used in 2 autopilot suites. Eventhough the same message type are being shared in both the suites, the accomendating behavior might differ: Ardupilot MAVlink usage PX4 MAVlink usage","title":"MAVlink"},{"location":"middleware_and_drivers/","text":"Middleware and Drivers The aim for this page is to provide a collection of ROS packages/related libraries which are very useful for aerial ROS projects either to serve as a reference or directly use as a part of a custom software stack. This list aims to address the gap between the flight controller firmware and purely autonomy related packages. Middleware libraries Enabling low overhead publish/subscribe on microcontrollers: Micro-XRCE-DDS Zenoh PX4-FastRTPS - superceded by Micro-XRCE-DDS (https://docs.px4.io/main/en/middleware/uxrce_dds.html) Driver packages for drone platforms ROS packages built atop SDKs from drone vendors to interface to their closed-source flight controller firmwares. Several of these may be for much older ROS1 distros but can have utility in terms of serving as references. Parrot ARDrone Autonomy Parrot Bebop Autonomy DJI Tello driver Parrot Mambo driver","title":"Middleware and Drivers"},{"location":"middleware_and_drivers/#middleware-and-drivers","text":"The aim for this page is to provide a collection of ROS packages/related libraries which are very useful for aerial ROS projects either to serve as a reference or directly use as a part of a custom software stack. This list aims to address the gap between the flight controller firmware and purely autonomy related packages.","title":"Middleware and Drivers"},{"location":"middleware_and_drivers/#middleware-libraries","text":"Enabling low overhead publish/subscribe on microcontrollers: Micro-XRCE-DDS Zenoh PX4-FastRTPS - superceded by Micro-XRCE-DDS (https://docs.px4.io/main/en/middleware/uxrce_dds.html)","title":"Middleware libraries"},{"location":"middleware_and_drivers/#driver-packages-for-drone-platforms","text":"ROS packages built atop SDKs from drone vendors to interface to their closed-source flight controller firmwares. Several of these may be for much older ROS1 distros but can have utility in terms of serving as references. Parrot ARDrone Autonomy Parrot Bebop Autonomy DJI Tello driver Parrot Mambo driver","title":"Driver packages for drone platforms"},{"location":"safety_management/","text":"Safety and Management Systems Quadcopter and other Aerial vehicles come in all forms and sizes. Safety systems are important for all, but the bigger an aerial vehicle becomes, the more important it becomes that the right fail-safes and emergency systems are in place! This pages gives an overview of those safety systems that have been implemented so far. Safety in ROS Currently specifically for aerial vehicles, the safety implementation is depended on the ROS autonomy stack used. The ROS-0147 has a suggestion for flightmodes for a statemachine that can simplify the understanding of it. It is also expecting that ROS will be run offboard on an external computer, and that it should be in control of the safety state machine. This should probably be discussed if it would be a good idea for ROS to be in control of this or the autopilot suites themselves. Safety in Autopilot suites Adapted from the following blogpost: https://www.bitcraze.io/2023/04/safety-and-the-brushless/ These are current Safety managements systems existing in Paparazzi UAV , Betaflight , ArduPilot and PX4 . The Crazyflie ecosystem also have some measures but are currently overhauling their safety framework now in the form of a supervisor. Pre-flight checks. Before a vehicle can fly, certain conditions must be met. These includ: calibrating and ensuring functionality of internal sensors (IMU, barometer, magnetometer) receiving a GPS signal converging the internal state estimator (usually an extended Kalman filter) to a position checking for remote control connection and datalink to a ground station conducting feasibility checks (e.g., mission parameters, start location proximity) confirming sufficient battery level, and ensuring the absence of error states from previous flights or crashes. Preflight checks documentation: PX4: https://docs.px4.io/main/en/flying/pre_flight_checks.html#preflight-sensor-estimator-checks ArduPilot: https://ardupilot.org/copter/docs/common-prearm-safety-checks.html#failure-messages Beta flight: https://betaflight.com/docs/wiki/archive/GPS-Rescue-v4-4#sanity-check-options Paparazzi UAV: Indicated per platform if necessary, on their wiki: https://wiki.paparazziuav.org/wiki/Failsafe Crazyflie firmware: https://www.bitcraze.io/documentation/repository/crazyflie-firmware/master/functional-areas/supervisor/ Fail-safe mechanisms After passing pre-flight checks and arming the UAV, the takeoff command is given. However, UAV flights have inherent risks, particularly during takeoff. To mitigate these risks, numerous safety features, known as failsafes, are implemented during the flight phase. These failsafes are categorized as triggers and behaviors, allowing developers to specify the UAV's response to different failures, such as initiating a safe landing in the event of GPS loss. Triggers Thus, there are triggers that can enable the autopilot\u2019s failsafe mechanics: No connection with the remote control No connection with the Ground station or Datalink Low Battery Position estimate diverges or full GPS loss Waypoint going beyond geofence or Mission is not feasible Other vehicles are nearby. Also, sometimes the support of an external Automatic Trigger system is required, which is a box that monitors the conditions where the UAV should take action in case there is no GPS, other aerial vehicles are nearby, or the UAV is crossing a geofence determined by outdoor flight restrictions. Note that all of these triggers usually have a couple of conditions attached, such as the level of the \u2018low battery\u2019 or the number of seconds of \u2018GPS loss\u2019 deemed acceptable. Fail-safe behaviors During UAV flights, safety features can be customized per trigger, deviating from the default actions set by regulations. Disarming the vehicle completely increases the risk of crashing and causing harm. Allowing the drone to autonomously complete the mission without intervention poses the risk of losing the vehicle or trespassing restricted areas. Modifying default behaviors should be undertaken by knowledgeable individuals with careful consideration. These behaviors can include the following: No action at all Warning on the console or remote control display Continue the mission autonomously Stay still at the same position or go to a home position Fly to a lower altitude Land based on position or safely land by reducing thrust No input to motors or completely disarming the motors Fail-safe documentation PX4: https://docs.px4.io/main/en/config/safety.html ArduPilot: https://ardupilot.org/copter/docs/failsafe-landing-page.html Betaflight: https://betaflight.com/docs/development/Failsafe Paparazzi UAV: https://wiki.paparazziuav.org/wiki/Failsafe Crazyflie firmware: https://www.bitcraze.io/documentation/repository/crazyflie-firmware/master/functional-areas/supervisor/ Emergencies Fail-safes ensure safe flight, but emergencies like crashes, flips, or hardware failures can still occur. In such cases, the standard default action is to disarm the vehicle to prevent unintended motor activation. Backup systems connected to ESCs may take over if the autopilot becomes unresponsive. The pilot plays a vital role in safety, with the remote control featuring a dedicated button or switch for different modes, enabling actions like landing or disarming. It's recommended to have a net or towel to stop spinning motors and to promptly disconnect the battery. Being prepared for potential LiPo battery hazards is essential, with sand or fire retardant on hand. While autopilots provide guidance, conducting thorough research on handling emergencies, spinning parts, and LiPo battery fires is crucial. Here a list of that: * Remote control should have a dedicated button/switch for different modes, landing, or disarming. * Dealing with spinning motors Use a net or towel to stop them and promptly disconnect the battery. * To prepare for LiPo battery hazards, Have sand or fire retardant available.","title":"Safety and Management Systems"},{"location":"safety_management/#safety-and-management-systems","text":"Quadcopter and other Aerial vehicles come in all forms and sizes. Safety systems are important for all, but the bigger an aerial vehicle becomes, the more important it becomes that the right fail-safes and emergency systems are in place! This pages gives an overview of those safety systems that have been implemented so far.","title":"Safety and Management Systems"},{"location":"safety_management/#safety-in-ros","text":"Currently specifically for aerial vehicles, the safety implementation is depended on the ROS autonomy stack used. The ROS-0147 has a suggestion for flightmodes for a statemachine that can simplify the understanding of it. It is also expecting that ROS will be run offboard on an external computer, and that it should be in control of the safety state machine. This should probably be discussed if it would be a good idea for ROS to be in control of this or the autopilot suites themselves.","title":"Safety in ROS"},{"location":"safety_management/#safety-in-autopilot-suites","text":"Adapted from the following blogpost: https://www.bitcraze.io/2023/04/safety-and-the-brushless/ These are current Safety managements systems existing in Paparazzi UAV , Betaflight , ArduPilot and PX4 . The Crazyflie ecosystem also have some measures but are currently overhauling their safety framework now in the form of a supervisor.","title":"Safety in Autopilot suites"},{"location":"safety_management/#pre-flight-checks","text":"Before a vehicle can fly, certain conditions must be met. These includ: calibrating and ensuring functionality of internal sensors (IMU, barometer, magnetometer) receiving a GPS signal converging the internal state estimator (usually an extended Kalman filter) to a position checking for remote control connection and datalink to a ground station conducting feasibility checks (e.g., mission parameters, start location proximity) confirming sufficient battery level, and ensuring the absence of error states from previous flights or crashes. Preflight checks documentation: PX4: https://docs.px4.io/main/en/flying/pre_flight_checks.html#preflight-sensor-estimator-checks ArduPilot: https://ardupilot.org/copter/docs/common-prearm-safety-checks.html#failure-messages Beta flight: https://betaflight.com/docs/wiki/archive/GPS-Rescue-v4-4#sanity-check-options Paparazzi UAV: Indicated per platform if necessary, on their wiki: https://wiki.paparazziuav.org/wiki/Failsafe Crazyflie firmware: https://www.bitcraze.io/documentation/repository/crazyflie-firmware/master/functional-areas/supervisor/","title":"Pre-flight checks."},{"location":"safety_management/#fail-safe-mechanisms","text":"After passing pre-flight checks and arming the UAV, the takeoff command is given. However, UAV flights have inherent risks, particularly during takeoff. To mitigate these risks, numerous safety features, known as failsafes, are implemented during the flight phase. These failsafes are categorized as triggers and behaviors, allowing developers to specify the UAV's response to different failures, such as initiating a safe landing in the event of GPS loss.","title":"Fail-safe mechanisms"},{"location":"safety_management/#triggers","text":"Thus, there are triggers that can enable the autopilot\u2019s failsafe mechanics: No connection with the remote control No connection with the Ground station or Datalink Low Battery Position estimate diverges or full GPS loss Waypoint going beyond geofence or Mission is not feasible Other vehicles are nearby. Also, sometimes the support of an external Automatic Trigger system is required, which is a box that monitors the conditions where the UAV should take action in case there is no GPS, other aerial vehicles are nearby, or the UAV is crossing a geofence determined by outdoor flight restrictions. Note that all of these triggers usually have a couple of conditions attached, such as the level of the \u2018low battery\u2019 or the number of seconds of \u2018GPS loss\u2019 deemed acceptable.","title":"Triggers"},{"location":"safety_management/#fail-safe-behaviors","text":"During UAV flights, safety features can be customized per trigger, deviating from the default actions set by regulations. Disarming the vehicle completely increases the risk of crashing and causing harm. Allowing the drone to autonomously complete the mission without intervention poses the risk of losing the vehicle or trespassing restricted areas. Modifying default behaviors should be undertaken by knowledgeable individuals with careful consideration. These behaviors can include the following: No action at all Warning on the console or remote control display Continue the mission autonomously Stay still at the same position or go to a home position Fly to a lower altitude Land based on position or safely land by reducing thrust No input to motors or completely disarming the motors Fail-safe documentation PX4: https://docs.px4.io/main/en/config/safety.html ArduPilot: https://ardupilot.org/copter/docs/failsafe-landing-page.html Betaflight: https://betaflight.com/docs/development/Failsafe Paparazzi UAV: https://wiki.paparazziuav.org/wiki/Failsafe Crazyflie firmware: https://www.bitcraze.io/documentation/repository/crazyflie-firmware/master/functional-areas/supervisor/","title":"Fail-safe behaviors"},{"location":"safety_management/#emergencies","text":"Fail-safes ensure safe flight, but emergencies like crashes, flips, or hardware failures can still occur. In such cases, the standard default action is to disarm the vehicle to prevent unintended motor activation. Backup systems connected to ESCs may take over if the autopilot becomes unresponsive. The pilot plays a vital role in safety, with the remote control featuring a dedicated button or switch for different modes, enabling actions like landing or disarming. It's recommended to have a net or towel to stop spinning motors and to promptly disconnect the battery. Being prepared for potential LiPo battery hazards is essential, with sand or fire retardant on hand. While autopilots provide guidance, conducting thorough research on handling emergencies, spinning parts, and LiPo battery fires is crucial. Here a list of that: * Remote control should have a dedicated button/switch for different modes, landing, or disarming. * Dealing with spinning motors Use a net or towel to stop them and promptly disconnect the battery. * To prepare for LiPo battery hazards, Have sand or fire retardant available.","title":"Emergencies"},{"location":"simulation/","text":"Simulation of Aerial Robotics Simulation is crucial when working with Unmanned Aerial Vehicles (UAVs). Testing different trajectories and control paradigms in a simulator before implementing them on the real platform ensures not only safety but also facilitates development. This page presents several simulator options for aerial robotics enthusiasts. Comparison The following simulators have their own integrated physics simulation and basic rendering capabilities. They are capable of simulating the aerodynamic forces necessary to keep UAVs airborne: For the following comparison, we have refered this article Cora A. Dimmig et al. \"Survey of Simulators for Aerial Robots\" arXiv preprint arXiv:2311.02296v2 (2024) Features List migh not be complete. If you see any error or missing components, feel free to open a PR or issue. Name Physics Engine Rendering Linux[^1] Windows[^1] MacOS[^1] Interface (S/H)ITL[^6] Active[^2] Hardware requirement[^3] Licence Open source[^4] Interest [^5] Gazebo ( RotorS , CrazyS , PX4 SITL ) ODE/ Bullet/ DART/ Simbody OGRE \u2713 ( \u2713 \u2713 \u2713 ) \u2731 ( \u2717 \u2717 \u2717) \u2713 ( \u2717 \u2717 \u2717 ) ROS 1/2, C++, RL PX4, ArduPilot, CF ^7 \u2713 ( \u2717 \u2731 \u2717 ) minimal/decent Apache 2.0 \u2713 High Gazebo Bullet/ DART/ TPE OGRE \u2713 \u2731 \u2713 ROS 1/2, C++, Python, RL PX4, ArduPilot, CF \u2713 minimal/decent Apache 2.0 \u2713 High Isaac ( Pegasus , Aerial Gym ) NVIDIA PhysX/ Flex Vulkan \u2713 \u2717 \u2717 ROS 1/2, Python, RL Pegasus: PX4 \u2713 high/demanding NVIDIA OMNIVERSE (BSD 3) \u2717 (\u2713 \u2713) User specific Webots ODE OpenGL \u2713 \u2713 \u2713 ROS 1/2, C/C++, Python, MATLAB, Java ArduPilot, CF \u2713 decent/high Apache 2.0 \u2713 Developing CoppeliaSim Bullet/ODE/Vortex/Newton/MuJoCo OpenGL \u2713 \u2713 \u2713 ROS 1/2, C/C++, Python, MATLAB, Java,Lua,Octave -- \u2713 decent/high GNU GPL/Commercial \u2731 Decent AIRsim NVIDIA PhysX Unreal,Unity \u2713 \u2713 \u2713 ROS 1, C++, Python, C#, Java,RL PX4, ArduPilot \u2717 medium/high MIT \u2713 Low Flightmare Ad hoc, Gazebo classic Unity \u2713 \u2717 \u2717 ROS 1, C++, RL -- \u2717 -- MIT \u2713 Low FlightGoggles Ad hoc Unity \u2713 \u2731 \u2717 ROS 1, C++ Motion capture \u2717 -- MIT \u2713 Unknown Gym-pybullet-drones Pybullet OpenGL \u2713 \u2731 \u2713 Python, RL Betaflight, CF \u2713 minimal/decent/high MIT (Pybullet: zlib ) \u2713 High RotorTM Ad hoc OpenGL \u2713 \u2717 \u2717 ROS 1, Python, MATLAB -- \u2713 -- GNU GPL \u2713 Unknown MATLAB UAV Toolbox MATLAB Unreal \u2713 \u2713 \u2713 ROS 2, MATLAB PX4 \u2713 -- Proprietary, Commercial \u2717 Unknown O3de NVIDIA PhysX/ NVIDIA Cloth/ AMD TressFX Atom \u2713 \u2713 \u2731 ROS 2[^8] , C++ unknown \u2713 decent/high Apache-2.0/MIT \u2713 Developing Drake ad hoc unknown \u2713 \u2717 \u2713 C++, Python, ROS 2 unknown \u2713 unknown BSD 3 \u2713 Developing Flightgear unknown unknown \u2713 \u2713 \u2713 C++ unknown \u2713 minimal/decent GNU-GPL \u2713 Low RealFlight unknown unknown \u2717 \u2713 \u2717 -- unknown \u2713 minimal/decent non-public \u2717 Low RotorPy ad hoc unknown \u2713 \u2713 \u2713 Python -- \u2713 minimal/decent MIT \u2713 Developing [^1]: \u2713: Full support, \u2731: Partial support, \u2717: No support [^2]: \u2713: Active and maintained, \u2731: Inactive but responding to issues/ PR, \u2717: Inactive for 2+ years [^3]: For a referance, a laptop running Intel i5 10th gen (or similar) with 8gb ddr4 ram and NVIDIA T100 4gb (or similar) are considered as minimal requirement. [^4]: \u2713: Yes, \u2731: Yes for non commercial use-case , \u2717: No [^5]: Usage in Aerial ROS/Robotics community according to several survey on Discourse and during the meetings. [^6]: (Software/Hardware) In The Loop [^8]: It seem under development and there is some docs out there. Vehicle types Simulator Multirotor (Basic) Multirotor (Drag) Multirotor (Wind) Fixed-wings Aerial Manipulators Swarms Cars Other vehicles Gazebo (Classic & New) \u2713 \u2713 \u2713 \u2713 \u2731 \u2731 \u2713 \u2713 Isaac (Pegasus, Aerial Gym) \u2713 \u2717(\u2713,\u2717) \u2717 \u2717 \u2717 \u2713 \u2713(\u2717,\u2717) \u2713(\u2717,\u2717) Webots \u2713 \u2717 \u2717 \u2717 \u2717 \u2731 \u2713 \u2713 CoppeliaSim \u2713 \u2713 \u2731 \u2717 \u2731 \u2731 \u2713 \u2713 AirSim \u2713 \u2713 \u2713 \u2717 \u2717 \u2731 \u2713 \u2717 Flightmare \u2713 \u2713 \u2717 \u2717 \u2717 \u2713 \u2717 \u2717 FlightGoggles \u2713 \u2713 \u2717 \u2717 \u2717 \u2717 \u2713 \u2717 gym-pybullet-drones \u2713 \u2713 \u2717 \u2717 \u2717 \u2713 \u2717 \u2717 RotorTM \u2713 \u2717 \u2717 \u2717 \u2713 \u2713 \u2717 \u2717 MATLAB UAV Toolbox \u2713 \u2713 \u2713 \u2713 \u2717 \u2731 \u2717 \u2717 O3de* ~ ? ? ? ? ? ~ ~ Drake* ? ? ? ? ? ? ? ? RotorPy* ~ ~ ~ \u2717 \u2717 ~ \u2717 \u2717 [ * ] : Unknown data. If you have information on specific topic, please comment bellow with the referance link. [ ~ ] : Yes according to my knowledge but it needs development. [ ? ] : Unknown [ \u2717 ] : No according to my research. [ \u2713 ] : Yes [ \u2731 ] : Yes. But not specifically designed for it [ \u2717 ] : No Sensor support TODO Flight dynamics models Some simulators mostly focus on creating accurate dynamics for aerial vehicles. Here are some options: JSBSim ( https://github.com/JSBSim-Team/jsbsim ) YASim ( https://wiki.flightgear.org/YASim ) More to be added! Available UAV Models Each simulator typically offers a range of ready-to-use aerial vehicle models: Gazebo : https://app.gazebosim.org/search;q=uav Webots : https://webots.cloud/proto?keyword=robot%2Fflying Autopilot Suites Several autopilot suites provide instructions for using simulators, often with Software-in-the-Loop (SITL) or Hardware-in-the-Loop (HITL) options: Ardupilot : https://ardupilot.org/copter/docs/common-simulation.html Betaflight : https://betaflight.com/docs/development/SITL#sitl-in-realflight-9 Crazyflie : https://www.bitcraze.io/documentation/tutorials/getting-started-with-simulation/ DJI : https://www.dji.com/se/simulator Paparazzi UAV : https://wiki.paparazziuav.org/wiki/Simulation PX4 : https://docs.px4.io/main/en/simulation/#simulation ROSflight : https://docs.rosflight.org/v1.3/user-guide/gazebo_simulation/","title":"Simulation of Aerial Robotics"},{"location":"simulation/#simulation-of-aerial-robotics","text":"Simulation is crucial when working with Unmanned Aerial Vehicles (UAVs). Testing different trajectories and control paradigms in a simulator before implementing them on the real platform ensures not only safety but also facilitates development. This page presents several simulator options for aerial robotics enthusiasts.","title":"Simulation of Aerial Robotics"},{"location":"simulation/#comparison","text":"The following simulators have their own integrated physics simulation and basic rendering capabilities. They are capable of simulating the aerodynamic forces necessary to keep UAVs airborne: For the following comparison, we have refered this article Cora A. Dimmig et al. \"Survey of Simulators for Aerial Robots\" arXiv preprint arXiv:2311.02296v2 (2024)","title":"Comparison"},{"location":"simulation/#features","text":"List migh not be complete. If you see any error or missing components, feel free to open a PR or issue. Name Physics Engine Rendering Linux[^1] Windows[^1] MacOS[^1] Interface (S/H)ITL[^6] Active[^2] Hardware requirement[^3] Licence Open source[^4] Interest [^5] Gazebo ( RotorS , CrazyS , PX4 SITL ) ODE/ Bullet/ DART/ Simbody OGRE \u2713 ( \u2713 \u2713 \u2713 ) \u2731 ( \u2717 \u2717 \u2717) \u2713 ( \u2717 \u2717 \u2717 ) ROS 1/2, C++, RL PX4, ArduPilot, CF ^7 \u2713 ( \u2717 \u2731 \u2717 ) minimal/decent Apache 2.0 \u2713 High Gazebo Bullet/ DART/ TPE OGRE \u2713 \u2731 \u2713 ROS 1/2, C++, Python, RL PX4, ArduPilot, CF \u2713 minimal/decent Apache 2.0 \u2713 High Isaac ( Pegasus , Aerial Gym ) NVIDIA PhysX/ Flex Vulkan \u2713 \u2717 \u2717 ROS 1/2, Python, RL Pegasus: PX4 \u2713 high/demanding NVIDIA OMNIVERSE (BSD 3) \u2717 (\u2713 \u2713) User specific Webots ODE OpenGL \u2713 \u2713 \u2713 ROS 1/2, C/C++, Python, MATLAB, Java ArduPilot, CF \u2713 decent/high Apache 2.0 \u2713 Developing CoppeliaSim Bullet/ODE/Vortex/Newton/MuJoCo OpenGL \u2713 \u2713 \u2713 ROS 1/2, C/C++, Python, MATLAB, Java,Lua,Octave -- \u2713 decent/high GNU GPL/Commercial \u2731 Decent AIRsim NVIDIA PhysX Unreal,Unity \u2713 \u2713 \u2713 ROS 1, C++, Python, C#, Java,RL PX4, ArduPilot \u2717 medium/high MIT \u2713 Low Flightmare Ad hoc, Gazebo classic Unity \u2713 \u2717 \u2717 ROS 1, C++, RL -- \u2717 -- MIT \u2713 Low FlightGoggles Ad hoc Unity \u2713 \u2731 \u2717 ROS 1, C++ Motion capture \u2717 -- MIT \u2713 Unknown Gym-pybullet-drones Pybullet OpenGL \u2713 \u2731 \u2713 Python, RL Betaflight, CF \u2713 minimal/decent/high MIT (Pybullet: zlib ) \u2713 High RotorTM Ad hoc OpenGL \u2713 \u2717 \u2717 ROS 1, Python, MATLAB -- \u2713 -- GNU GPL \u2713 Unknown MATLAB UAV Toolbox MATLAB Unreal \u2713 \u2713 \u2713 ROS 2, MATLAB PX4 \u2713 -- Proprietary, Commercial \u2717 Unknown O3de NVIDIA PhysX/ NVIDIA Cloth/ AMD TressFX Atom \u2713 \u2713 \u2731 ROS 2[^8] , C++ unknown \u2713 decent/high Apache-2.0/MIT \u2713 Developing Drake ad hoc unknown \u2713 \u2717 \u2713 C++, Python, ROS 2 unknown \u2713 unknown BSD 3 \u2713 Developing Flightgear unknown unknown \u2713 \u2713 \u2713 C++ unknown \u2713 minimal/decent GNU-GPL \u2713 Low RealFlight unknown unknown \u2717 \u2713 \u2717 -- unknown \u2713 minimal/decent non-public \u2717 Low RotorPy ad hoc unknown \u2713 \u2713 \u2713 Python -- \u2713 minimal/decent MIT \u2713 Developing [^1]: \u2713: Full support, \u2731: Partial support, \u2717: No support [^2]: \u2713: Active and maintained, \u2731: Inactive but responding to issues/ PR, \u2717: Inactive for 2+ years [^3]: For a referance, a laptop running Intel i5 10th gen (or similar) with 8gb ddr4 ram and NVIDIA T100 4gb (or similar) are considered as minimal requirement. [^4]: \u2713: Yes, \u2731: Yes for non commercial use-case , \u2717: No [^5]: Usage in Aerial ROS/Robotics community according to several survey on Discourse and during the meetings. [^6]: (Software/Hardware) In The Loop [^8]: It seem under development and there is some docs out there.","title":"Features"},{"location":"simulation/#vehicle-types","text":"Simulator Multirotor (Basic) Multirotor (Drag) Multirotor (Wind) Fixed-wings Aerial Manipulators Swarms Cars Other vehicles Gazebo (Classic & New) \u2713 \u2713 \u2713 \u2713 \u2731 \u2731 \u2713 \u2713 Isaac (Pegasus, Aerial Gym) \u2713 \u2717(\u2713,\u2717) \u2717 \u2717 \u2717 \u2713 \u2713(\u2717,\u2717) \u2713(\u2717,\u2717) Webots \u2713 \u2717 \u2717 \u2717 \u2717 \u2731 \u2713 \u2713 CoppeliaSim \u2713 \u2713 \u2731 \u2717 \u2731 \u2731 \u2713 \u2713 AirSim \u2713 \u2713 \u2713 \u2717 \u2717 \u2731 \u2713 \u2717 Flightmare \u2713 \u2713 \u2717 \u2717 \u2717 \u2713 \u2717 \u2717 FlightGoggles \u2713 \u2713 \u2717 \u2717 \u2717 \u2717 \u2713 \u2717 gym-pybullet-drones \u2713 \u2713 \u2717 \u2717 \u2717 \u2713 \u2717 \u2717 RotorTM \u2713 \u2717 \u2717 \u2717 \u2713 \u2713 \u2717 \u2717 MATLAB UAV Toolbox \u2713 \u2713 \u2713 \u2713 \u2717 \u2731 \u2717 \u2717 O3de* ~ ? ? ? ? ? ~ ~ Drake* ? ? ? ? ? ? ? ? RotorPy* ~ ~ ~ \u2717 \u2717 ~ \u2717 \u2717 [ * ] : Unknown data. If you have information on specific topic, please comment bellow with the referance link. [ ~ ] : Yes according to my knowledge but it needs development. [ ? ] : Unknown [ \u2717 ] : No according to my research. [ \u2713 ] : Yes [ \u2731 ] : Yes. But not specifically designed for it [ \u2717 ] : No","title":"Vehicle types"},{"location":"simulation/#sensor-support","text":"TODO","title":"Sensor support"},{"location":"simulation/#flight-dynamics-models","text":"Some simulators mostly focus on creating accurate dynamics for aerial vehicles. Here are some options: JSBSim ( https://github.com/JSBSim-Team/jsbsim ) YASim ( https://wiki.flightgear.org/YASim ) More to be added!","title":"Flight dynamics models"},{"location":"simulation/#available-uav-models","text":"Each simulator typically offers a range of ready-to-use aerial vehicle models: Gazebo : https://app.gazebosim.org/search;q=uav Webots : https://webots.cloud/proto?keyword=robot%2Fflying","title":"Available UAV Models"},{"location":"simulation/#autopilot-suites","text":"Several autopilot suites provide instructions for using simulators, often with Software-in-the-Loop (SITL) or Hardware-in-the-Loop (HITL) options: Ardupilot : https://ardupilot.org/copter/docs/common-simulation.html Betaflight : https://betaflight.com/docs/development/SITL#sitl-in-realflight-9 Crazyflie : https://www.bitcraze.io/documentation/tutorials/getting-started-with-simulation/ DJI : https://www.dji.com/se/simulator Paparazzi UAV : https://wiki.paparazziuav.org/wiki/Simulation PX4 : https://docs.px4.io/main/en/simulation/#simulation ROSflight : https://docs.rosflight.org/v1.3/user-guide/gazebo_simulation/","title":"Autopilot Suites"}]} \ No newline at end of file diff --git a/search/worker.js b/search/worker.js new file mode 100644 index 0000000..8628dbc --- /dev/null +++ b/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? 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+ + +
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+ +

Simulation of Aerial Robotics

+

Simulation is crucial when working with Unmanned Aerial Vehicles (UAVs). Testing different trajectories and control paradigms in a simulator before implementing them on the real platform ensures not only safety but also facilitates development.

+

This page presents several simulator options for aerial robotics enthusiasts.

+

Comparison

+

The following simulators have their own integrated physics simulation and basic rendering capabilities. They are capable of simulating the aerodynamic forces necessary to keep UAVs airborne:

+

For the following comparison, we have refered this article

+
+

Cora A. Dimmig et al. "Survey of Simulators for Aerial Robots" arXiv preprint arXiv:2311.02296v2 (2024)

+
+

Features

+

List migh not be complete. If you see any error or missing components, feel free to open a PR or issue.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NamePhysics EngineRenderingLinux[^1]Windows[^1]MacOS[^1]Interface(S/H)ITL[^6]Active[^2]Hardware requirement[^3]LicenceOpen source[^4]Interest [^5]
Gazebo (
RotorS,
CrazyS,
PX4 SITL)		ODE/ Bullet/ DART/ SimbodyOGRE✓
( ✓
✓
✓ )		✱
( ✗
✗
✗)		✓
( ✗
✗
✗ )		ROS 1/2, C++, RLPX4, ArduPilot, CF^7✓
( ✗
✱
✗ )		minimal/decentApache 2.0✓High
GazeboBullet/ DART/ TPEOGRE✓✱✓ROS 1/2, C++, Python, RLPX4, ArduPilot, CF✓minimal/decentApache 2.0✓High
Isaac (
Pegasus,
Aerial Gym)		NVIDIA PhysX/ FlexVulkan✓✗✗ROS 1/2, Python, RLPegasus: PX4✓high/demandingNVIDIA OMNIVERSE
(BSD 3)		✗
(✓
✓)		User specific
WebotsODEOpenGL✓✓✓ROS 1/2, C/C++, Python, MATLAB, JavaArduPilot, CF✓decent/highApache 2.0✓Developing
CoppeliaSimBullet/ODE/Vortex/Newton/MuJoCoOpenGL✓✓✓ROS 1/2, C/C++, Python, MATLAB, Java,Lua,Octave--✓decent/highGNU GPL/Commercial✱Decent
AIRsimNVIDIA PhysXUnreal,Unity✓✓✓ROS 1, C++, Python, C#, Java,RLPX4, ArduPilot✗medium/highMIT✓Low
FlightmareAd hoc, Gazebo classicUnity✓✗✗ROS 1, C++, RL--✗--MIT✓Low
FlightGogglesAd hocUnity✓✱✗ROS 1, C++Motion capture✗--MIT✓Unknown
Gym-pybullet-dronesPybulletOpenGL✓✱✓Python, RLBetaflight, CF✓minimal/decent/highMIT (Pybullet: zlib)✓High
RotorTMAd hocOpenGL✓✗✗ROS 1, Python, MATLAB--✓--GNU GPL✓Unknown
MATLAB UAV ToolboxMATLABUnreal✓✓✓ROS 2, MATLABPX4✓--Proprietary, Commercial✗Unknown
O3deNVIDIA PhysX/ NVIDIA Cloth/ AMD TressFXAtom✓✓✱ROS 2[^8] , C++unknown✓decent/highApache-2.0/MIT✓Developing
Drakead hocunknown✓✗✓C++, Python, ROS 2unknown✓unknownBSD 3✓Developing
Flightgearunknownunknown✓✓✓C++unknown✓minimal/decentGNU-GPL✓Low
RealFlightunknownunknown✗✓✗--unknown✓minimal/decentnon-public✗Low
RotorPyad hocunknown✓✓✓Python--✓minimal/decentMIT✓Developing
+

[^1]: ✓: Full support, ✱: Partial support, ✗: No support

+

[^2]: ✓: Active and maintained, ✱: Inactive but responding to issues/ PR, ✗: Inactive for 2+ years

+

[^3]: For a referance, a laptop running Intel i5 10th gen (or similar) with 8gb ddr4 ram and NVIDIA T100 4gb (or similar) are considered as minimal requirement.

+

[^4]: ✓: Yes, ✱: Yes for non commercial use-case , ✗: No

+

[^5]: Usage in Aerial ROS/Robotics community according to several survey on Discourse and during the meetings.

+

[^6]: (Software/Hardware) In The Loop

+

[^8]: It seem under development and there is some docs out there.

+

Vehicle types

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SimulatorMultirotor (Basic)Multirotor (Drag)Multirotor (Wind)Fixed-wingsAerial ManipulatorsSwarmsCarsOther vehicles
Gazebo (Classic & New)✓✓✓✓✱✱✓✓
Isaac (Pegasus, Aerial Gym)✓✗(✓,✗)✗✗✗✓✓(✗,✗)✓(✗,✗)
Webots✓✗✗✗✗✱✓✓
CoppeliaSim✓✓✱✗✱✱✓✓
AirSim✓✓✓✗✗✱✓✗
Flightmare✓✓✗✗✗✓✗✗
FlightGoggles✓✓✗✗✗✗✓✗
gym-pybullet-drones✓✓✗✗✗✓✗✗
RotorTM✓✗✗✗✓✓✗✗
MATLAB UAV Toolbox✓✓✓✓✗✱✗✗
O3de*~?????~~
Drake*????????
RotorPy*~~~✗✗~✗✗
+
    +
  • [ * ] : Unknown data. If you have information on specific topic, please comment bellow with the referance link.
      +
    • [ ~ ] : Yes according to my knowledge but it needs development.
      • +
    • [ ? ] : Unknown
      • +
    • [ ✗ ] : No according to my research.
      • +
    +
    • +
  • [ ✓ ] : Yes
    • +
  • [ ✱ ] : Yes. But not specifically designed for it
    • +
  • [ ✗ ] : No
    • +
+

Sensor support

+

TODO

+

Flight dynamics models

+

Some simulators mostly focus on creating accurate dynamics for aerial vehicles. Here are some options:

+ +

Available UAV Models

+

Each simulator typically offers a range of ready-to-use aerial vehicle models:

+ +

Autopilot Suites

+

Several autopilot suites provide instructions for using simulators, often with Software-in-the-Loop (SITL) or Hardware-in-the-Loop (HITL) options:

+ + + +
+
+ + +
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