文件列表:

.lgtm.yml (57, 2019-06-03)
.travis.yml (950, 2019-06-03)
AerialNavigation (0, 2019-06-03)
AerialNavigation\drone_3d_trajectory_following (0, 2019-06-03)
AerialNavigation\drone_3d_trajectory_following\Quadrotor.py (2572, 2019-06-03)
AerialNavigation\drone_3d_trajectory_following\TrajectoryGenerator.py (2103, 2019-06-03)
AerialNavigation\drone_3d_trajectory_following\drone_3d_trajectory_following.py (5560, 2019-06-03)
AerialNavigation\rocket_powered_landing (0, 2019-06-03)
AerialNavigation\rocket_powered_landing\figure.png (688548, 2019-06-03)
AerialNavigation\rocket_powered_landing\rocket_powered_landing.ipynb (30258, 2019-06-03)
AerialNavigation\rocket_powered_landing\rocket_powered_landing.py (24451, 2019-06-03)
ArmNavigation (0, 2019-06-03)
ArmNavigation\__init__.py (0, 2019-06-03)
ArmNavigation\arm_obstacle_navigation (0, 2019-06-03)
ArmNavigation\arm_obstacle_navigation\arm_obstacle_navigation.py (8488, 2019-06-03)
ArmNavigation\arm_obstacle_navigation\arm_obstacle_navigation_2.py (9626, 2019-06-03)
ArmNavigation\n_joint_arm_to_point_control (0, 2019-06-03)
ArmNavigation\n_joint_arm_to_point_control\NLinkArm.py (2300, 2019-06-03)
ArmNavigation\n_joint_arm_to_point_control\n_joint_arm_to_point_control.py (5273, 2019-06-03)
ArmNavigation\two_joint_arm_to_point_control (0, 2019-06-03)
ArmNavigation\two_joint_arm_to_point_control\Planar_Two_Link_IK.ipynb (94195, 2019-06-03)
ArmNavigation\two_joint_arm_to_point_control\two_joint_arm_to_point_control.py (3256, 2019-06-03)
Bipedal (0, 2019-06-03)
Bipedal\__init__.py (0, 2019-06-03)
Bipedal\bipedal_planner (0, 2019-06-03)
Bipedal\bipedal_planner\bipedal_planner.py (7248, 2019-06-03)
CONTRIBUTING.md (787, 2019-06-03)
Introduction (0, 2019-06-03)
Introduction\introduction.ipynb (699, 2019-06-03)
LICENSE (1080, 2019-06-03)
Localization (0, 2019-06-03)
Localization\Kalmanfilter_basics.ipynb (150483, 2019-06-03)
Localization\Kalmanfilter_basics_2.ipynb (1466276, 2019-06-03)
Localization\bayes_filter.png (1088102, 2019-06-03)
Localization\extended_kalman_filter (0, 2019-06-03)
Localization\extended_kalman_filter\ekf.png (113010, 2019-06-03)
... ...

# PythonRobotics [](https://travis-ci.org/AtsushiSakai/PythonRobotics) [](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest) [](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) [](https://coveralls.io/github/AtsushiSakai/PythonRobotics?branch=master) [](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) [](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master) [](https://github.com/AtsushiSakai/PythonRobotics) [](https://saythanks.io/to/AtsushiSakai) 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) * [Pose Optimization SLAM](#pose-optimization-slam) * [Path Planning](#path-planning) * [Dynamic Window Approach](#dynamic-window-approach) * [Grid based search](#grid-based-search) * [Dijkstra algorithm](#dijkstra-algorithm) * [A* algorithm](#a-algorithm) * [Potential Field algorithm](#potential-field-algorithm) * [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-sheep path](#rrt-with-reeds-sheep-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) * [Support](#support) * [Authors](#authors) # What is this? This is a Python code collection of robotics algorithms, especially for autonomous navigation. 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 - Python 3.6.x (2.7 is not supported) - numpy - scipy - matplotlib - pandas - [cvxpy](http://www.cvxpy.org/en/latest/) # 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 > cd PythonRobotics/ 2. Install the required libraries. You can use environment.yml with conda command. > conda env create -f environment.yml 3. Execute python script in each directory. 4. Add star to this repo if you like it :smiley:. # Localization ## Extended Kalman Filter localization 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 estimated trajectory with PF. It is assumed that the robot can measure a distance from landmarks (RFID). This 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.  ## Ray casting grid map This is a 2D ray casting grid mapping example.  ## Lidar to grid map This example shows how to convert a 2D range measurement to a grid map.  ## k-means object clustering This is a 2D object clustering with k-means algorithm.  ## Rectangle fitting This is a 2D rectangle fitting for vehicle detection.  # SLAM Simultaneous Localization and Mapping(SLAM) examples ## Iterative Closest Point (ICP) Matching This is a 2D ICP matching example with singular value decomposition. It can calculate a rotation matrix and a translation vector between points to points.  Ref: - [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf) ## FastSLAM 1.0 This is a feature based SLAM example using FastSLAM 1.0. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. The red points are particles of FastSLAM. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.  Ref: - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) - [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm) ## Pose Optimization SLAM This is a graph based pose optimization SLAM example.  # Path Planning ## Dynamic Window Approach This is a 2D navigation sample code with Dynamic Window Approach. - [The Dynamic Window Approach to Collision Avoidance](https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf)  ## Grid based search ### Dijkstra algorithm This is a 2D grid based shortest path planning with Dijkstra's algorithm.  In the animation, cyan points are searched nodes. ### A\* algorithm This is a 2D grid based shortest path planning with A star algorithm.  In the animation, cyan points are searched nodes. Its heuristic is 2D Euclid distance. ### Potential Field algorithm This is a 2D grid based path planning with Potential Field algorithm.  In the animation, the blue heat map shows potential value on each grid. Ref: - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf) ## State Lattice Planning This script is a path planning code with state lattice planning. This code uses the model predictive trajectory generator to solve boundary problem. Ref: - [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/02783***906075328) - [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf) ### Biased polar sampling  ### Lane sampling  ## Probabilistic Road-Map (PRM) planning  This PRM planner uses Dijkstra method for graph search. In the animation, blue points are sampled points, Cyan crosses means searched points with Dijkstra method, The red line is the final path of PRM. Ref: - [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap) ## Rapidly-Exploring Random Trees (RRT) ### RRT\*  This is a path planning code with RRT\* Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. Ref: - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416) - [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf) ### RRT\* with reeds-sheep path ) Path planning for a car robot with RRT\* and reeds sheep path planner. ### LQR-RRT\* This is a path planning simulation with LQR-RRT\*. A double integrator motion model is used for LQR local planner.  Ref: - [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](http://lis.csail.mit.edu/pubs/perez-icra12.pdf) - [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) ## Quintic polynomials planning Motion planning with quintic polynomials.  It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials. Ref: - [Local Path Planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/) ## Reeds Shepp planning A sample code with Reeds Shepp path planning.  Ref: - [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html) - [optimal paths for a car that goes both forwards and backwards](https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf) - [ghliu/pyReedsShepp: Implementation of Reeds Shepp curve\.](https://github.com/ghliu/pyReedsShepp) ## LQR based path planning A sample code using LQR based path planning for double integrator model.  ## Optimal Trajectory in a Frenet Frame  This is optimal trajectory generation in a Frenet Frame. The cyan line is the target course and black crosses are obstacles. The red line is predicted path. Ref: - [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/54f749df0cf2103***e9277af.pdf) - [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame](https://www.youtube.com/watch?v=Cj6tAQe7UCY) # Path Tracking ## move to a pose control This is a simulation of moving to a pose control  Ref: - [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-***2-20144-8) ## Stanley control Path tracking simulation with Stanley steering control and PID speed control.  Ref: - [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf) - [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) ## Rear wheel feedback control Path tracking simulation with rear wheel feedback steering control and PID speed control.  Ref: - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446) ## Linear–quadratic regulator (LQR) speed and steering control Path tracking simulation with LQR speed and steering control.  Ref: - [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](http://ieeexplore.ieee.org/document/5940562/) ## Model predictive speed and steering control Path tracking simulation with iterative linear model predictive speed and steering control. Ref: - [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb) ## Nonlinear Model predictive control with C-GMRES A motion planning and path tracking simulation with NMPC of C-GMRES  Ref: - [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb) # Arm Navigation ## N joint arm to point control N joint arm to a point control simulation. This is a interactive simulation. You can set the goal position of the end effector with left-click on the ploting 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. ![3](https://github.com/AtsushiSakai/ ... ...

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