From 5f8a2edce22f02e0a786bd0fa79c9082a3cd2d91 Mon Sep 17 00:00:00 2001 From: Antonin RAFFIN Date: Sat, 15 Sep 2018 15:33:13 +0200 Subject: [PATCH] Clean up README --- README.md | 734 +----------------------------------------------------- 1 file changed, 8 insertions(+), 726 deletions(-) diff --git a/README.md b/README.md index c942dc1b..0642ee73 100644 --- a/README.md +++ b/README.md @@ -10,69 +10,7 @@ Python codes for robotics algorithm. * [What is this?](#what-is-this) * [Requirements](#requirements) * [How to use](#how-to-use) - * [Localization](#localization) - * [Extended Kalman Filter localization](#extended-kalman-filter-localization) - * [Unscented Kalman Filter localization](#unscented-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) - * [k-means object clustering](#k-means-object-clustering) - * [Object shape recognition using circle fitting](#object-shape-recognition-using-circle-fitting) - * [SLAM](#slam) - * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching) - * [EKF SLAM](#ekf-slam) - * [FastSLAM 1.0](#fastslam-10) - * [FastSLAM 2.0](#fastslam-20) - * [Graph based SLAM](#graph-based-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) - * [Model Predictive Trajectory Generator](#model-predictive-trajectory-generator) - * [Path optimization sample](#path-optimization-sample) - * [Lookup table generation sample](#lookup-table-generation-sample) - * [State Lattice Planning](#state-lattice-planning) - * [Uniform polar sampling](#uniform-polar-sampling) - * [Biased polar sampling](#biased-polar-sampling) - * [Lane sampling](#lane-sampling) - * [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning) - * [Voronoi Road-Map planning](#voronoi-road-map-planning) - * [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt) - * [Basic RRT](#basic-rrt) - * [RRT*](#rrt) - * [RRT with dubins path](#rrt-with-dubins-path) - * [RRT* with dubins path](#rrt-with-dubins-path-1) - * [RRT* with reeds-sheep path](#rrt-with-reeds-sheep-path) - * [Informed RRT*](#informed-rrt) - * [Batch Informed RRT*](#batch-informed-rrt) - * [Closed Loop RRT*](#closed-loop-rrt) - * [LQR-RRT*](#lqr-rrt) - * [Cubic spline planning](#cubic-spline-planning) - * [B-Spline planning](#b-spline-planning) - * [Eta^3 Spline path planning](#eta3-spline-path-planning) - * [Bezier path planning](#bezier-path-planning) - * [Quintic polynomials planning](#quintic-polynomials-planning) - * [Dubins path planning](#dubins-path-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) - * [Pure pursuit tracking](#pure-pursuit-tracking) - * [Stanley control](#stanley-control) - * [Rear wheel feedback control](#rear-wheel-feedback-control) - * [Linear–quadratic regulator (LQR) steering control](#linearquadratic-regulator-lqr-steering-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) - * [Arm Navigation](#arm-navigation) - * [Two joint arm to point control](#two-joint-arm-to-point-control) - * [N joint arm to point control](#n-joint-arm-to-point-control) - * [Aerial Navigation](#aerial-navigation) - * [drone 3d trajectory following](#drone-3d-trajectory-following) + * [Documentation](#documentation) * [License](#license) * [Contribution](#contribution) * [Support](#support) @@ -94,20 +32,18 @@ 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)) +# Documentation + +Documentation is available online: [https://pythonrobotics.readthedocs.io/](https://pythonrobotics.readthedocs.io/) # Requirements - Python 3.6.x - - numpy - - scipy - - matplotlib - - pandas - -- [cvxpy](http://www.cvxpy.org/en/latest/) +- [cvxpy](http://www.cvxpy.org/en/latest/) # How to use @@ -117,661 +53,10 @@ See this paper for more details: 3. Execute python script in each directory. -4. Add star to this repo if you like it :smiley:. +4. Add star to this repo if you like it :smiley:. -# Localization -## Extended Kalman Filter localization - - - -This is a sensor fusion localization with Extended Kalman Filter(EKF). - -The blue line is true trajectory, the black line is dead reckoning trajectory, - -the green point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF. - -The red ellipse is estimated covariance ellipse with EKF. - -Ref: - -- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) - -## Unscented Kalman Filter localization - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/unscented_kalman_filter/animation.gif) - -This is a sensor fusion localization with Unscented Kalman Filter(UKF). - -The lines and points are same meaning of the EKF simulation. - -Ref: - -- [Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization](https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization) - - -## Particle filter localization - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/particle_filter/animation.gif) - -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 - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/histogram_filter/animation.gif) - -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://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/gaussian_grid_map/animation.gif) - -## Ray casting grid map - -This is a 2D ray casting grid mapping example. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/raycasting_grid_map/animation.gif) - -## k-means object clustering - -This is a 2D object clustering with k-means algorithm. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/kmeans_clustering/animation.gif) - -## Object shape recognition using circle fitting - -This is an object shape recognition using circle fitting. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/circle_fitting/animation.gif) - -The blue circle is the true object shape. - -The red crosses are observations from a ranging sensor. - -The red circle is the estimated object shape using circle fitting. - -# 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. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/iterative_closest_point/animation.gif) - -Ref: - -- [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf) - - -## EKF SLAM - -This is an Extended Kalman Filter based SLAM example. - -The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with EKF SLAM. - -The green crosses are estimated landmarks. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/EKFSLAM/animation.gif) - -Ref: - -- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) - -## 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. - - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM1/animation.gif) - - -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) - -## FastSLAM 2.0 - -This is a feature based SLAM example using FastSLAM 2.0. - -The animation has the same meanings as one of FastSLAM 1.0. - - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM2/animation.gif) - - -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) - - -## Graph based SLAM - -This is a graph based SLAM example. - -The blue line is ground truth. - -The black line is dead reckoning. - -The red line is the estimated trajectory with Graph based SLAM. - -The black stars are landmarks for graph edge generation. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/GraphBasedSLAM/animation.gif) - -Ref: - -- [A Tutorial on Graph-Based SLAM](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf) - - -# 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) - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/DynamicWindowApproach/animation.gif) - - -## Grid based search - -### Dijkstra algorithm - -This is a 2D grid based shortest path planning with Dijkstra's algorithm. - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/Dijkstra/animation.gif) - -In the animation, cyan points are searched nodes. - -### A\* algorithm - -This is a 2D grid based shortest path planning with A star algorithm. - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/AStar/animation.gif) - -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. - -![PotentialField](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif) - -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) - - -## Model Predictive Trajectory Generator - -This is a path optimization sample on model predictive trajectory generator. - -This algorithm is used for state lattice planner. - -### Path optimization sample - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif) - -### Lookup table generation sample - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True) - -Ref: - -- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328) - - -## 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/10.1177/0278364906075328) - -- [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) - - -### Uniform polar sampling - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif) - - -### Biased polar sampling - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif) - - -### Lane sampling - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif) - -## Probabilistic Road-Map (PRM) planning - -![PRM](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif) - -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) - -　　 -## Voronoi Road-Map planning - -![VRM](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/VoronoiRoadMap/animation.gif) - -This Voronoi road-map planner uses Dijkstra method for graph search. - -In the animation, blue points are Voronoi points, - -Cyan crosses mean searched points with Dijkstra method, - -The red line is the final path of Vornoi Road-Map. - -Ref: - -- [Robotic Motion Planning](https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf) - - -## Rapidly-Exploring Random Trees (RRT) - -### Basic RRT - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRT/animation.gif) - -This is a simple path planning code with Rapidly-Exploring Random Trees (RRT) - -Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. - -### RRT\* - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTstar/animation.gif) - -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 dubins path - -![PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTDubins/animation.gif) - -Path planning for a car robot with RRT and dubins path planner. - - -### RRT\* with dubins path - -![AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTStarDubins/animation.gif) - -Path planning for a car robot with RRT\* and dubins path planner. - - -### RRT\* with reeds-sheep path - -![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif)) - -Path planning for a car robot with RRT\* and reeds sheep path planner. - -### Informed RRT\* - -![irrt](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/InformedRRTStar/animation.gif)) - -This is a path planning code with Informed RRT\*. - -The cyan ellipse is the heuristic sampling domain of Informed RRT\*. - -Ref: - -- [Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://arxiv.org/pdf/1404.2334.pdf) - -### Batch Informed RRT\* - -![irrt](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif)) - -This is a path planning code with Batch Informed RRT\*. - -Ref: - -- [Batch Informed Trees \(BIT\*\): Sampling\-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs](https://arxiv.org/abs/1405.5848) - -### Closed Loop RRT\* - -A vehicle model based path planning with closed loop RRT\*. - -![CLRRT](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif) - -In this code, pure-pursuit algorithm is used for steering control, - -PID is used for speed control. - -Ref: - -- [Motion Planning in Complex Environments -using Closed-loop Prediction](http://acl.mit.edu/papers/KuwataGNC08.pdf) - -- [Real-time Motion Planning with Applications to -Autonomous Urban Driving](http://acl.mit.edu/papers/KuwataTCST09.pdf) - -- [[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction](https://arxiv.org/abs/1601.06326) - -### LQR-RRT\* - -This is a path planning simulation with LQR-RRT\*. - -A double integrator motion model is used for LQR local planner. - -![LQRRRT](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/LQRRRTStar/animation.gif) - -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) - - -## Cubic spline planning - -A sample code for cubic path planning. - -This code generates a curvature continuous path based on x-y waypoints with cubic spline. - -Heading angle of each point can be also calculated analytically. - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_1.png?raw=True) -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_2.png?raw=True) -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_3.png?raw=True) - - -## B-Spline planning - -![B-Spline](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BSplinePath/Figure_1.png?raw=True) - -This is a path planning with B-Spline curse. - -If you input waypoints, it generates a smooth path with B-Spline curve. - -The final course should be on the first and last waypoints. - -Ref: - -- [B\-spline \- Wikipedia](https://en.wikipedia.org/wiki/B-spline) - -## Eta^3 Spline path planning - -![eta3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/Eta3SplinePath/animation.gif?raw=True) - -This is a path planning with Eta^3 spline. - -Ref: - -- [\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots](https://ieeexplore.ieee.org/document/4339545/) - - - -## Bezier path planning - -A sample code of Bezier path planning. - -It is based on 4 control points Beier path. - -![Bezier1](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_1.png?raw=True) - -If you change the offset distance from start and end point, - -You can get different Beizer course: - -![Bezier2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_2.png?raw=True) - - -Ref: - -- [Continuous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf) - -## Quintic polynomials planning - -Motion planning with quintic polynomials. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif) - -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/) - - -## Dubins path planning - -A sample code for Dubins path planning. - -![dubins](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True) - -Ref: - -- [Dubins path - Wikipedia](https://en.wikipedia.org/wiki/Dubins_path) - -## Reeds Shepp planning - -A sample code with Reeds Shepp path planning. - -![RSPlanning](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true) - -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. - -![RSPlanning](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true) - - -## Optimal Trajectory in a Frenet Frame - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif) - -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/54f749df0cf210398e9277af.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 - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/move_to_pose/animation.gif) - -Ref: - -- [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8) - - -## Pure pursuit tracking - -Path tracking simulation with pure pursuit steering control and PID speed control. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/pure_pursuit/animation.gif) - -The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking. - -Ref: - -- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446) - -## Stanley control - -Path tracking simulation with Stanley steering control and PID speed control. - -![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/stanley_controller/animation.gif) - -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. - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/rear_wheel_feedback/animation.gif) - -Ref: - -- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446) - - -## Linear–quadratic regulator (LQR) steering control - -Path tracking simulation with LQR steering control and PID speed control. - -![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/lqr_steer_control/animation.gif) - -Ref: - -- [ApolloAuto/apollo: An open autonomous driving platform](https://github.com/ApolloAuto/apollo) - - -## Linear–quadratic regulator (LQR) speed and steering control - -Path tracking simulation with LQR speed and steering control. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/lqr_speed_steer_control/animation.gif) - -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/notebook.ipynb) - - -# Arm Navigation - -## Two joint arm to point control - -Two 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. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif) - - -## 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. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif) - -In this simulation N = 10, however, you can change it. - -# Aerial Navigation - -## drone 3d trajectory following - -This is a 3d trajectory following simulation for a quadrotor. - -![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif) - - -# License +# License MIT @@ -795,7 +80,7 @@ E-mail consultant is also available. 　 -Your comment using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) is also welcome. +Your comment using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) is also welcome. This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md) @@ -812,6 +97,3 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/ - [Antonin RAFFIN](https://github.com/araffin) - [Alexis Paques](https://github.com/AlexisTM) - - -