AtsushiSakai/PythonRobotics
PythonRobotics
PythonRobotics este o colecție cuprinzătoare de algoritmi modulari de robotică și simulări educaționale concepute pentru navigație autonomă, estimarea stării și controlul mișcării. Proiectul oferă o bibliotecă de implementări independente pentru planificarea traseului, localizare, mapare și cinematică, servind ca resursă pentru cercetători și studenți pentru a experimenta teorii robotice fundamentale și avansate.
Proiectul se distinge printr-un design centrat pe algoritm, unde fiecare modul funcționează ca un script izolat, permițând testarea independentă și demonstrația pedagogică clară. Fiecare implementare este mapată explicit la literatura academică sau manualele fundamentale de robotică, asigurându-se că modelele matematice și strategiile de control rămân verificabile și precise. Utilizatorii pot executa aceste scenarii într-un mediu de simulare decuplat care își menține propria stare internă și bucle de control, nefiind necesare dependențe externe.
Suprafața de capabilități acoperă o gamă largă de domenii robotice, inclusiv navigația aeriană, locomoția bipedă și controlul brațelor cu mai multe articulații. Dispune de seturi de instrumente extinse pentru fuziunea probabilistică a senzorilor, maparea mediului și urmărirea traiectoriei, toate alimentate de calcul numeric de înaltă performanță. Animațiile geometrice în timp real și estimările de stare sunt redate direct din datele de simulare folosind biblioteci standard de plotare.
Features
- Algorithm Libraries - Provides a comprehensive library of fundamental algorithms for autonomous robotic systems.
- Kinematics - Provides interactive simulation tools for multi-joint robotic arm control, including end-effector positioning and obstacle avoidance.
- Robotics Algorithms - Provides modular, standalone implementations of core robotics algorithms for educational purposes.
- SLAM Algorithms - Implements simultaneous localization and mapping using feature-based particle filtering and iterative point cloud matching.
- 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.
- Probabilistic Localization - Provides Kalman, histogram, and particle filters for robot position estimation.
- Sensor Fusion - Combines noisy sensor data with probabilistic filtering for robot localization.
- 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\.
- Path Tracking Control - Implements LQR, MPC, and feedback steering for autonomous vehicle guidance.
- 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.
- Framework-uri pentru robotică - Colecție de algoritmi de robotică pentru navigație autonomă.
- Scientific Computing - Compilation of robotics algorithms.
- Control Theory Libraries - Comprehensive collection of robotics algorithms and control simulations.
- Robotics Algorithms - Comprehensive collection of robotics algorithms implemented in Python.
- Robotics Frameworks - Collection of robotics algorithms implemented in Python.
- Reference Lists - Python sample code for robotics algorithms.
- Robotics Education - Collection of sample code for common robotic algorithms.
- Scientific Computing - Listed in the “Scientific Computing” section of the Awesome Python awesome list.
- Educational Robotics Libraries - Provides instructional simulations for teaching autonomous navigation concepts.
- Kinematics Simulators - Models multi-joint robotic arms and bipedal systems for movement simulation.
- Environmental Mapping Techniques - Provides grid-based occupancy, ray casting, and geometric shape fitting for spatial awareness.
- Bipedal Locomotion Planning - Optimizes footstep sequences for stable walking using inverted pendulum models.
- Hybrid Motion Planning - Path planning for a car robot with RRT\* and reeds shepp path planner.
- 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
- Numerical Libraries - Performs core mathematical operations and linear algebra using high-performance array processing.
- Nonlinear Optimization Solvers - A motion planning and path tracking simulation with NMPC of C-GMRES Reference - documentation
- 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.
- 3D Math and Geometry Toolkits - Renders real-time geometric animations and state estimations from simulation data using standard plotting libraries.
- Steering Control Strategies - Provides Stanley steering control implementations for autonomous path tracking.
- 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.
