用Python实现的机器人相关算法-python

<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/> # PythonRobotics   [](https://travis-ci.org/AtsushiSakai/PythonRobotics) [](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest) [](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) [](https://codecov.io/gh/AtsushiSakai/PythonRobotics) [](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) [](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master) [](https://github.com/AtsushiSakai/PythonRobotics) Python codes for robotics algorithm. # Table of Contents * [What is this?](#what-is-this) * [Requirements](#requirements) * [Documentation](#documentation) * [How to use](#how-to-use) * [Localization](#localization) * [Extended Kalman Filter localization](#extended-kalman-filter-localization) * [Particle filter localization](#particle-filter-localization) * [Histogram filter localization](#histogram-filter-localization) * [Mapping](#mapping) * [Gaussian grid map](#gaussian-grid-map) * [Ray casting grid map](#ray-casting-grid-map) * [Lidar to grid map](#lidar-to-grid-map) * [k-means object clustering](#k-means-object-clustering) * [Rectangle fitting](#rectangle-fitting) * [SLAM](#slam) * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching) * [FastSLAM 1.0](#fastslam-10) * [Path Planning](#path-planning) * [Dynamic Window Approach](#dynamic-window-approach) * [Grid based search](#grid-based-search) * [Dijkstra algorithm](#dijkstra-algorithm) * [A* algorithm](#a-algorithm) * [D* algorithm](#d-algorithm) * [D* Lite algorithm](#d-lite-algorithm) * [Potential Field algorithm](#potential-field-algorithm) * [Grid based coverage path planning](#grid-based-coverage-path-planning) * [State Lattice Planning](#state-lattice-planning) * [Biased polar sampling](#biased-polar-sampling) * [Lane sampling](#lane-sampling) * [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning) * [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt) * [RRT*](#rrt) * [RRT* with reeds-shepp path](#rrt-with-reeds-shepp-path) * [LQR-RRT*](#lqr-rrt) * [Quintic polynomials planning](#quintic-polynomials-planning) * [Reeds Shepp planning](#reeds-shepp-planning) * [LQR based path planning](#lqr-based-path-planning) * [Optimal Trajectory in a Frenet Frame](#optimal-trajectory-in-a-frenet-frame) * [Path Tracking](#path-tracking) * [move to a pose control](#move-to-a-pose-control) * [Stanley control](#stanley-control) * [Rear wheel feedback control](#rear-wheel-feedback-control) * [Linear–quadratic regulator (LQR) speed and steering control](#linearquadratic-regulator-lqr-speed-and-steering-control) * [Model predictive speed and steering control](#model-predictive-speed-and-steering-control) * [Nonlinear Model predictive control with C-GMRES](#nonlinear-model-predictive-control-with-c-gmres) * [Arm Navigation](#arm-navigation) * [N joint arm to point control](#n-joint-arm-to-point-control) * [Arm navigation with obstacle avoidance](#arm-navigation-with-obstacle-avoidance) * [Aerial Navigation](#aerial-navigation) * [drone 3d trajectory following](#drone-3d-trajectory-following) * [rocket powered landing](#rocket-powered-landing) * [Bipedal](#bipedal) * [bipedal planner with inverted pendulum](#bipedal-planner-with-inverted-pendulum) * [License](#license) * [Use-case](#use-case) * [Contribution](#contribution) * [Citing](#citing) * [Support](#support) * [Sponsors](#Sponsors) * [JetBrains](#JetBrains) * [Authors](#authors) # What is this? This is a Python code collection of robotics algorithms. Features: 1. Easy to read for understanding each algorithm's basic idea. 2. Widely used and practical algorithms are selected. 3. Minimum dependency. See this paper for more details: - [\[1808\.10703\] 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)) # Requirements For running each sample code: - Python 3.9.x - numpy - scipy - matplotlib - pandas - [cvxpy](https://www.cvxpy.org/index.html) For development: - pytest (for unit tests) - pytest-xdist (for parallel unit tests) - mypy (for type check) - Sphinx (for document generation) - pycodestyle (for code style check) # Documentation This README only shows some examples of this project. If you are interested in other examples or mathematical backgrounds of each algorithm, You can check the full documentation online: [https://pythonrobotics.readthedocs.io/](https://pythonrobotics.readthedocs.io/) All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs) # How to use 1. Clone this repo. > git clone https://github.com/AtsushiSakai/PythonRobotics.git 2. Install the required libraries. using conda : > conda env create -f environment.yml using pip : > pip install -r requirements.txt 3. Execute python script in each directory. 4. Add star to this repo if you like it :smiley:. # Localization ## Extended Kalman Filter localization <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic"> Documentation: [Notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb) ## Particle filter localization  This is a sensor fusion localization with Particle Filter(PF). The blue line is true trajectory, the black line is dead reckoning trajectory, and the red line is an estimated trajectory with PF. It is assumed that the robot can measure a distance from landmarks (RFID). These measurements are used for PF localization. Ref: - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) ## Histogram filter localization  This is a 2D localization example with Histogram filter. The red cross is true position, black points are RFID positions. The blue grid shows a position probability of histogram filter. In this simulation, x,y are unknown, yaw is known. The filter integrates speed input and range observations from RFID for localization. Initial position is not needed. Ref: - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) # Mapping ## Gaussian grid map This is a 2D Gaussian grid mapping example. ![2](https://