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659 lines
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Markdown
659 lines
23 KiB
Markdown
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>
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# PythonRobotics
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[](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
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Python codes and [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) for robotics algorithm.
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# Table of Contents
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* [What is this?](#what-is-this)
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* [Requirements](#requirements)
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* [Documentation](#documentation)
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* [How to use](#how-to-use)
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* [Localization](#localization)
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* [Extended Kalman Filter localization](#extended-kalman-filter-localization)
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* [Particle filter localization](#particle-filter-localization)
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* [Histogram filter localization](#histogram-filter-localization)
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* [Mapping](#mapping)
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* [Gaussian grid map](#gaussian-grid-map)
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* [Ray casting grid map](#ray-casting-grid-map)
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* [Lidar to grid map](#lidar-to-grid-map)
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* [k-means object clustering](#k-means-object-clustering)
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* [Rectangle fitting](#rectangle-fitting)
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* [SLAM](#slam)
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* [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
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* [FastSLAM 1.0](#fastslam-10)
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* [Path Planning](#path-planning)
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* [Dynamic Window Approach](#dynamic-window-approach)
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* [Grid based search](#grid-based-search)
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* [Dijkstra algorithm](#dijkstra-algorithm)
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* [A* algorithm](#a-algorithm)
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* [D* algorithm](#d-algorithm)
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* [D* Lite algorithm](#d-lite-algorithm)
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* [Potential Field algorithm](#potential-field-algorithm)
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* [Grid based coverage path planning](#grid-based-coverage-path-planning)
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* [State Lattice Planning](#state-lattice-planning)
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* [Biased polar sampling](#biased-polar-sampling)
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* [Lane sampling](#lane-sampling)
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* [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning)
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* [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt)
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* [RRT*](#rrt)
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* [RRT* with reeds-shepp path](#rrt-with-reeds-shepp-path)
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* [LQR-RRT*](#lqr-rrt)
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* [Quintic polynomials planning](#quintic-polynomials-planning)
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* [Reeds Shepp planning](#reeds-shepp-planning)
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* [LQR based path planning](#lqr-based-path-planning)
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* [Optimal Trajectory in a Frenet Frame](#optimal-trajectory-in-a-frenet-frame)
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* [Path Tracking](#path-tracking)
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* [move to a pose control](#move-to-a-pose-control)
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* [Stanley control](#stanley-control)
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* [Rear wheel feedback control](#rear-wheel-feedback-control)
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* [Linear–quadratic regulator (LQR) speed and steering control](#linearquadratic-regulator-lqr-speed-and-steering-control)
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* [Model predictive speed and steering control](#model-predictive-speed-and-steering-control)
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* [Nonlinear Model predictive control with C-GMRES](#nonlinear-model-predictive-control-with-c-gmres)
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* [Arm Navigation](#arm-navigation)
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* [N joint arm to point control](#n-joint-arm-to-point-control)
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* [Arm navigation with obstacle avoidance](#arm-navigation-with-obstacle-avoidance)
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* [Aerial Navigation](#aerial-navigation)
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* [drone 3d trajectory following](#drone-3d-trajectory-following)
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* [rocket powered landing](#rocket-powered-landing)
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* [Bipedal](#bipedal)
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* [bipedal planner with inverted pendulum](#bipedal-planner-with-inverted-pendulum)
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* [License](#license)
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* [Use-case](#use-case)
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* [Contribution](#contribution)
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* [Citing](#citing)
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* [Support](#support)
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* [Sponsors](#sponsors)
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* [JetBrains](#JetBrains)
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* [1Password](#1password)
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* [Authors](#authors)
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# What is PythonRobotics?
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PythonRobotics is a Python code collection and a [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) of robotics algorithms.
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Features:
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1. Easy to read for understanding each algorithm's basic idea.
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2. Widely used and practical algorithms are selected.
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3. Minimum dependency.
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See this documentation
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- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/1_what_is_python_robotics.html)
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or this Youtube video:
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- [PythonRobotics project audio overview](https://www.youtube.com/watch?v=uMeRnNoJAfU)
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or this paper for more details:
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- [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
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# Requirements to run the code
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For running each sample code:
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- [Python 3.13.x](https://www.python.org/)
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- [NumPy](https://numpy.org/)
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- [SciPy](https://scipy.org/)
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- [Matplotlib](https://matplotlib.org/)
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- [cvxpy](https://www.cvxpy.org/)
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For development:
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- [pytest](https://pytest.org/) (for unit tests)
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- [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests)
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- [mypy](https://mypy-lang.org/) (for type check)
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- [sphinx](https://www.sphinx-doc.org/) (for document generation)
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- [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check)
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# Documentation (Textbook)
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This README only shows some examples of this project.
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If you are interested in other examples or mathematical backgrounds of each algorithm,
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You can check the full documentation (textbook) online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html)
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All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs)
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# How to use
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1. Clone this repo.
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```terminal
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git clone https://github.com/AtsushiSakai/PythonRobotics.git
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```
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2. Install the required libraries.
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- using conda :
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```terminal
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conda env create -f requirements/environment.yml
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```
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- using pip :
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```terminal
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pip install -r requirements/requirements.txt
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```
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3. Execute python script in each directory.
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4. Add star to this repo if you like it :smiley:.
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# Localization
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## Extended Kalman Filter localization
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<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
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Reference
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- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
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## Particle filter localization
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This is a sensor fusion localization with Particle Filter(PF).
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The blue line is true trajectory, the black line is dead reckoning trajectory,
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and the red line is an estimated trajectory with PF.
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It is assumed that the robot can measure a distance from landmarks (RFID).
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These measurements are used for PF localization.
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Reference
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- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
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## Histogram filter localization
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This is a 2D localization example with Histogram filter.
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The red cross is true position, black points are RFID positions.
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The blue grid shows a position probability of histogram filter.
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In this simulation, x,y are unknown, yaw is known.
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The filter integrates speed input and range observations from RFID for localization.
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Initial position is not needed.
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Reference
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- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
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# Mapping
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## Gaussian grid map
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This is a 2D Gaussian grid mapping example.
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## Ray casting grid map
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This is a 2D ray casting grid mapping example.
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## Lidar to grid map
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This example shows how to convert a 2D range measurement to a grid map.
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## k-means object clustering
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This is a 2D object clustering with k-means algorithm.
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## Rectangle fitting
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This is a 2D rectangle fitting for vehicle detection.
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# SLAM
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Simultaneous Localization and Mapping(SLAM) examples
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## Iterative Closest Point (ICP) Matching
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This is a 2D ICP matching example with singular value decomposition.
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It can calculate a rotation matrix, and a translation vector between points and points.
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Reference
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- [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf)
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## FastSLAM 1.0
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This is a feature based SLAM example using FastSLAM 1.0.
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The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM.
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The red points are particles of FastSLAM.
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Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.
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Reference
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- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
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- [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
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# Path Planning
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## Dynamic Window Approach
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This is a 2D navigation sample code with Dynamic Window Approach.
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- [The Dynamic Window Approach to Collision Avoidance](https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf)
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## Grid based search
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### Dijkstra algorithm
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This is a 2D grid based the shortest path planning with Dijkstra's algorithm.
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In the animation, cyan points are searched nodes.
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### A\* algorithm
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This is a 2D grid based the shortest path planning with A star algorithm.
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In the animation, cyan points are searched nodes.
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Its heuristic is 2D Euclid distance.
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### D\* algorithm
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This is a 2D grid based the shortest path planning with D star algorithm.
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The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
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Reference
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- [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
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### D\* Lite algorithm
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This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. It has been implemented here for a 2D grid.
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The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm.
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Refs:
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- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf)
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- [Improved Fast Replanning for Robot Navigation in Unknown Terrain](http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
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### Potential Field algorithm
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This is a 2D grid based path planning with Potential Field algorithm.
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In the animation, the blue heat map shows potential value on each grid.
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Reference
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- [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
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### Grid based coverage path planning
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This is a 2D grid based coverage path planning simulation.
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## State Lattice Planning
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This script is a path planning code with state lattice planning.
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This code uses the model predictive trajectory generator to solve boundary problem.
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Reference
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- [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
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- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](https://www.cs.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
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### Biased polar sampling
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### Lane sampling
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## Probabilistic Road-Map (PRM) planning
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This PRM planner uses Dijkstra method for graph search.
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In the animation, blue points are sampled points,
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Cyan crosses means searched points with Dijkstra method,
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The red line is the final path of PRM.
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Reference
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- [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap)
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## Rapidly-Exploring Random Trees (RRT)
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### RRT\*
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This is a path planning code with RRT\*
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Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
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Reference
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- [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
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- [Sampling-based Algorithms for Optimal Motion Planning](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bddbc99f97173430aa49a0ada53ab5bade5902fa)
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### RRT\* with reeds-shepp path
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Path planning for a car robot with RRT\* and reeds shepp path planner.
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### LQR-RRT\*
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This is a path planning simulation with LQR-RRT\*.
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A double integrator motion model is used for LQR local planner.
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Reference
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- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf)
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- [MahanFathi/LQR\-RRTstar: LQR\-RRT\* method is used for random motion planning of a simple pendulum in its phase plot](https://github.com/MahanFathi/LQR-RRTstar)
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## Quintic polynomials planning
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Motion planning with quintic polynomials.
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It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials.
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Reference
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- [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
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## Reeds Shepp planning
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A sample code with Reeds Shepp path planning.
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Reference
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- [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html)
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- [optimal paths for a car that goes both forwards and backwards](https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf)
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- [ghliu/pyReedsShepp: Implementation of Reeds Shepp curve\.](https://github.com/ghliu/pyReedsShepp)
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## LQR based path planning
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A sample code using LQR based path planning for double integrator model.
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## Optimal Trajectory in a Frenet Frame
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This is optimal trajectory generation in a Frenet Frame.
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The cyan line is the target course and black crosses are obstacles.
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The red line is the predicted path.
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Reference
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- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
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- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame](https://www.youtube.com/watch?v=Cj6tAQe7UCY)
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# Path Tracking
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## move to a pose control
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This is a simulation of moving to a pose control
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Reference
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- [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8)
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## Stanley control
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Path tracking simulation with Stanley steering control and PID speed control.
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Reference
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- [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf)
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- [Automatic Steering Methods for Autonomous Automobile Path Tracking](https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf)
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## Rear wheel feedback control
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Path tracking simulation with rear wheel feedback steering control and PID speed control.
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Reference
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- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446)
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## Linear–quadratic regulator (LQR) speed and steering control
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Path tracking simulation with LQR speed and steering control.
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Reference
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- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/)
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## Model predictive speed and steering control
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Path tracking simulation with iterative linear model predictive speed and steering control.
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<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640" alt="MPC pic">
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Reference
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- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
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- [Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing](http://grauonline.de/wordpress/?page_id=3244)
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## Nonlinear Model predictive control with C-GMRES
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A motion planning and path tracking simulation with NMPC of C-GMRES
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Reference
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- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc.html)
|
||
|
||
|
||
# Arm Navigation
|
||
|
||
## N joint arm to point control
|
||
|
||
N joint arm to a point control simulation.
|
||
|
||
This is an interactive simulation.
|
||
|
||
You can set the goal position of the end effector with left-click on the plotting area.
|
||
|
||
|
||
|
||
In this simulation N = 10, however, you can change it.
|
||
|
||
## Arm navigation with obstacle avoidance
|
||
|
||
Arm navigation with obstacle avoidance simulation.
|
||
|
||
|
||
|
||
|
||
# Aerial Navigation
|
||
|
||
## drone 3d trajectory following
|
||
|
||
This is a 3d trajectory following simulation for a quadrotor.
|
||
|
||
|
||
|
||
## rocket powered landing
|
||
|
||
This is a 3d trajectory generation simulation for a rocket powered landing.
|
||
|
||
|
||
|
||
Reference
|
||
|
||
- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
|
||
|
||
# Bipedal
|
||
|
||
## bipedal planner with inverted pendulum
|
||
|
||
This is a bipedal planner for modifying footsteps for an inverted pendulum.
|
||
|
||
You can set the footsteps, and the planner will modify those automatically.
|
||
|
||
|
||
|
||
# License
|
||
|
||
MIT
|
||
|
||
# Use-case
|
||
|
||
If this project helps your robotics project, please let me know with creating an issue.
|
||
|
||
Your robot's video, which is using PythonRobotics, is very welcome!!
|
||
|
||
This is a list of user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
|
||
|
||
# Contribution
|
||
|
||
Any contribution is welcome!!
|
||
|
||
Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html)
|
||
|
||
# Citing
|
||
|
||
If you use this project's code for your academic work, we encourage you to cite [our papers](https://arxiv.org/abs/1808.10703)
|
||
|
||
If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly.
|
||
|
||
# <a id="support"></a>Supporting this project
|
||
|
||
If you or your company would like to support this project, please consider:
|
||
|
||
- [Sponsor @AtsushiSakai on GitHub Sponsors](https://github.com/sponsors/AtsushiSakai)
|
||
|
||
- [Become a backer or sponsor on Patreon](https://www.patreon.com/myenigma)
|
||
|
||
- [One-time donation via PayPal](https://www.paypal.com/paypalme/myenigmapay/)
|
||
|
||
If you would like to support us in some other way, please contact with creating an issue.
|
||
|
||
## <a id="sponsors"></a>Sponsors
|
||
|
||
### <a id="JetBrains"></a>[JetBrains](https://www.jetbrains.com/)
|
||
|
||
They are providing a free license of their IDEs for this OSS development.
|
||
|
||
### [1Password](https://github.com/1Password/for-open-source)
|
||
|
||
They are providing a free license of their 1Password team license for this OSS project.
|
||
|
||
|
||
# Authors
|
||
|
||
- [Contributors to AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/graphs/contributors)
|
||
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