AtsushiSakai/PythonRobotics
PythonRobotics
PythonRobotics es una colección completa de algoritmos de robótica modulares y simulaciones educativas diseñadas para la navegación autónoma, estimación de estados y control de movimiento. El proyecto proporciona una biblioteca de implementaciones independientes para planificación de rutas, localización, mapeo y cinemática, sirviendo como recurso para que investigadores y estudiantes experimenten con teorías robóticas fundamentales y avanzadas.
El proyecto se distingue por un diseño centrado en algoritmos donde cada módulo funciona como un script aislado, permitiendo pruebas independientes y una demostración pedagógica clara. Cada implementación está explícitamente mapeada a literatura académica o libros de texto fundamentales de robótica, asegurando que los modelos matemáticos y las estrategias de control sigan siendo verificables y precisos. Los usuarios pueden ejecutar estos escenarios dentro de un entorno de simulación desacoplado que mantiene su propio estado interno y bucles de control, sin requerir dependencias externas.
La superficie de capacidades cubre una amplia gama de dominios robóticos, incluyendo navegación aérea, locomoción bípeda y control de brazos multiarticulados. Cuenta con kits de herramientas extensos para fusión de sensores probabilísticos, mapeo ambiental y seguimiento de trayectorias, todo impulsado por computación numérica de alto rendimiento. Las animaciones geométricas en tiempo real y las estimaciones de estado se renderizan directamente a partir de datos de simulación utilizando bibliotecas de trazado estándar.
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.
- Frameworks de robótica - Colección de algoritmos de robótica para navegación autónoma.
- 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.
