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AtsushiSakai/PythonRobotics

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PythonRobotics

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Features

  • Autonomous Navigation - Implements motion planning and trajectory tracking strategies for autonomous vehicles.
  • Robotics Algorithm Libraries - Provides a comprehensive library of fundamental algorithms for autonomous robotic systems.
  • Robotics Kinematics - Provides interactive simulation tools for multi-joint robotic arm control, including end-effector positioning and obstacle avoidance.
  • Path Planning Algorithms - 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. Reference - Probab
  • Sampling-based Planning - 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. Reference - Incremental Sampling-based Algorithms for Optimal Motion Planning
  • Robotics Algorithms - Provides modular, standalone implementations of core robotics algorithms for educational purposes.
  • Motion Planning Toolkits - Calculates optimal paths and motion profiles for mobile robots and manipulators.
  • Navigation Frameworks - Provides a modular framework for obstacle avoidance and trajectory generation.
  • Path Planning Algorithms - Implements diverse path planning methods including sampling-based and optimization-driven approaches.
  • SLAM Algorithms - Implements simultaneous localization and mapping using feature-based particle filtering and iterative point cloud matching.
  • Path Tracking Control - Implements LQR, MPC, and feedback steering for autonomous vehicle guidance.
  • Probabilistic Localization - Provides Kalman, histogram, and particle filters for robot position estimation.
  • Sensor Fusion - Combines noisy sensor data with probabilistic filtering for robot localization.
  • Simultaneous Localization and Mapping - Implements FastSLAM 1.0 for particle-based trajectory estimation and mapping.
  • State Estimation Libraries - Provides probabilistic algorithms for sensor fusion and robot localization.
  • Control Systems - Path tracking simulation with iterative linear model predictive speed and steering control. Reference - documentation - Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing
  • Kinematic Path Planning - A sample code with Reeds Shepp path planning. Reference - 15.3.2 Reeds\-Shepp Curves - optimal paths for a car that goes both forwards and backwards - ghliu/pyReedsShepp: Implementation of Reeds Shepp curve\.
  • Motion Planning Profiles - Motion planning with quintic polynomials. It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials. Reference - Local Path Planning And Motion Control For Agv In Positioning
  • Path Tracking Controllers - Implements LQR-based speed and steering control for path tracking.
  • Sampling-Based Motion Planning - This is a path planning simulation with LQR-RRT\. A double integrator motion model is used for LQR local planner. Reference - LQR\-RRT\: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heur
  • Path Planning Algorithms - Implements A* algorithm for 2D grid-based shortest path planning.
  • Simulation Frameworks - Ships a decoupled simulation environment for testing robotic scenarios without external dependencies.
  • Educational Robotics Libraries - Provides instructional simulations for teaching autonomous navigation concepts.
  • Bipedal Locomotion Planning - Optimizes footstep sequences for stable walking using inverted pendulum models.
  • Environmental Mapping Techniques - Provides grid-based occupancy, ray casting, and geometric shape fitting for spatial awareness.
  • Kinematics Simulators - Models multi-joint robotic arms and bipedal systems for movement simulation.
  • Hybrid Motion Planning - Path planning for a car robot with RRT\* and reeds shepp path planner.
  • Nonlinear Optimization Solvers - A motion planning and path tracking simulation with NMPC of C-GMRES Reference - documentation
  • Numerical Computation Libraries - Performs core mathematical operations and linear algebra using high-performance array processing.
  • Potential Field Methods - 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. Reference - Robotic Motion Planning:Potential Functions
  • Trajectory Generation - 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 the predicted path. Reference - Optimal Trajectory Generation for Dynamic Stree
  • Kalman Filter Localization - Implements Extended Kalman Filter algorithms for robotic localization.
  • Particle Filter Localization - Provides sensor fusion localization using particle filter algorithms.
  • Aerial Navigation Simulators - Simulates three-dimensional trajectory following and rocket-powered landing maneuvers.
  • Point Cloud Registration - Provides 2D Iterative Closest Point matching using singular value decomposition.
  • Metaheuristic Optimization - This is a 2D path planning simulation using the Particle Swarm Optimization algorithm. PSO is a metaheuristic optimization algorithm inspired by bird flocking behavior. In path planning, particles explore the search spac
  • Aerial Navigation Simulations - Provides a 3D trajectory following simulation for quadrotor aerial navigation.
  • Arm Navigation Simulations - Simulates robotic arm navigation with integrated obstacle avoidance.
  • Bipedal Planning Simulations - Simulates bipedal footstep planning using inverted pendulum models.
  • Histogram Filter Localization - Demonstrates 2D localization using histogram filter probabilistic grid mapping.
  • Lidar Mapping - Converts 2D Lidar range measurements into occupancy grid maps.
  • Rocket Landing Simulations - Simulates 3D trajectory generation for rocket-powered landing scenarios.
  • Robotics Prototyping Environments - Provides a simulated environment for testing path planning and control algorithms.
  • Geometric Visualizers - Renders real-time geometric animations and state estimations from simulation data using standard plotting libraries.
  • Path Tracking Controllers - Implements feedback steering and PID speed control for autonomous vehicle path tracking.
  • Steering Control Strategies - Provides Stanley steering control implementations for autonomous path tracking.
  • Robotics Kinematics - This is a simulation of moving to a pose control Reference - P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102
  • Gaussian Grid Mapping - Demonstrates 2D Gaussian grid mapping for robotic environments.
  • Manipulator Control Simulations - Provides an interactive simulation for N-joint robotic arm end-effector control.
  • Ray Casting Mapping - Implements 2D ray casting techniques for grid map generation.
  • Sampling-Based Planning - Demonstrates biased polar sampling techniques for robotic path planning.
  • PythonRobotics is a comprehensive collection of modular robotics algorithms and educational simulations designed for autonomous navigation, state estimation, and motion control. The project provides a library of standalone implementations for path planning, localization, mapping, and kinematics, serving as a resource for researchers and students to experiment with foundational and advanced robotic theories.

    The project distinguishes itself through an algorithm-centric design where each module functions as an isolated script, allowing for independent testing and clear pedagogical demonstration. Every implementation is explicitly mapped to academic literature or foundational robotics textbooks, ensuring that the mathematical models and control strategies remain verifiable and accurate. Users can execute these scenarios within a decoupled simulation environment that maintains its own internal state and control loops, requiring no external dependencies.

    The capability surface covers a broad range of robotic domains, including aerial navigation, bipedal locomotion, and multi-joint arm control. It features extensive toolkits for probabilistic sensor fusion, environmental mapping, and trajectory tracking, all powered by high-performance numerical computation. Real-time geometric animations and state estimations are rendered directly from simulation data using standard plotting libraries.