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Title: GitHub - shinelord/PythonRobotics: Python sample codes for robotics algorithms.

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Description: Python sample codes for robotics algorithms. Contribute to shinelord/PythonRobotics development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#what-is-this
[1808.10703] PythonRobotics: a Python code collection of robotics algorithmshttps://arxiv.org/abs/1808.10703
BibTeXhttps://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#requirements
Python 3.11.xhttps://www.python.org/
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pycodestylehttps://pypi.org/project/pycodestyle/
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#documentation
Welcome to PythonRobotics’s documentation! — PythonRobotics documentationhttps://atsushisakai.github.io/PythonRobotics/index.html
AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRoboticshttps://github.com/AtsushiSakai/PythonRoboticsGifs
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#how-to-use
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#localization
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#extended-kalman-filter-localization
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
Notebookhttps://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#particle-filter-localization
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
PROBABILISTIC ROBOTICShttp://www.probabilistic-robotics.org/
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#histogram-filter-localization
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif
PROBABILISTIC ROBOTICShttp://www.probabilistic-robotics.org/
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#mapping
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#gaussian-grid-map
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#ray-casting-grid-map
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#lidar-to-grid-map
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#k-means-object-clustering
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rectangle-fitting
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#slam
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#iterative-closest-point-icp-matching
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
Introduction to Mobile Robotics: Iterative Closest Point Algorithmhttps://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#fastslam-10
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
PROBABILISTIC ROBOTICShttp://www.probabilistic-robotics.org/
SLAM simulations by Tim Baileyhttp://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#path-planning
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#dynamic-window-approach
The Dynamic Window Approach to Collision Avoidancehttps://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#grid-based-search
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#dijkstra-algorithm
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
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https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#d-algorithm
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
D* Algorithm Wikipediahttps://en.wikipedia.org/wiki/D*
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#d-lite-algorithm
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif
D* Litehttp://idm-lab.org/bib/abstracts/papers/aaai02b.pd
Improved Fast Replanning for Robot Navigation in Unknown Terrainhttp://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#potential-field-algorithm
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
Robotic Motion Planning:Potential Functionshttps://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#grid-based-coverage-path-planning
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#state-lattice-planning
Optimal rough terrain trajectory generation for wheeled mobile robotshttp://journals.sagepub.com/doi/pdf/10.1177/0278364906075328
State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environmentshttp://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#biased-polar-sampling
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#lane-sampling
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#probabilistic-road-map-prm-planning
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
Probabilistic roadmap - Wikipediahttps://en.wikipedia.org/wiki/Probabilistic_roadmap
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rapidly-exploring-random-trees-rrt
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rrt
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
Incremental Sampling-based Algorithms for Optimal Motion Planninghttps://arxiv.org/abs/1005.0416
Sampling-based Algorithms for Optimal Motion Planninghttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rrt-with-reeds-shepp-path
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#lqr-rrt
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristicshttp://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 plothttps://github.com/MahanFathi/LQR-RRTstar
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#quintic-polynomials-planning
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif
Local Path Planning And Motion Control For Agv In Positioninghttp://ieeexplore.ieee.org/document/637936/
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#reeds-shepp-planning
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
15.3.2 Reeds-Shepp Curveshttp://planning.cs.uiuc.edu/node822.html
optimal paths for a car that goes both forwards and backwardshttps://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf
ghliu/pyReedsShepp: Implementation of Reeds Shepp curve.https://github.com/ghliu/pyReedsShepp
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#lqr-based-path-planning
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#optimal-trajectory-in-a-frenet-frame
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Framehttps://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 Framehttps://www.youtube.com/watch?v=Cj6tAQe7UCY
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#path-tracking
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#move-to-a-pose-control
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
P. I. Corke, "Robotics, Vision and Control" | SpringerLink p102https://link.springer.com/book/10.1007/978-3-642-20144-8
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#stanley-control
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
Stanley: The robot that won the DARPA grand challengehttp://robots.stanford.edu/papers/thrun.stanley05.pdf
Automatic Steering Methods for Autonomous Automobile Path Trackinghttps://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rear-wheel-feedback-control
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicleshttps://arxiv.org/abs/1604.07446
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#linearquadratic-regulator-lqr-speed-and-steering-control
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publicationhttp://ieeexplore.ieee.org/document/5940562/
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#model-predictive-speed-and-steering-control
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif
notebookhttps://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playinghttp://grauonline.de/wordpress/?page_id=3244
https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#nonlinear-model-predictive-control-with-c-gmres
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
notebookhttps://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
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https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
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https://patch-diff.githubusercontent.com/shinelord/PythonRobotics#rocket-powered-landing
https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif
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How To Contribute — PythonRobotics documentationhttps://atsushisakai.github.io/PythonRobotics/how_to_contribute.html
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