Python sample codes and textbook for robotics algorithms.
Common used path planning algorithms with animations.
Motion planning and Navigation of AGV/AMR：ROS planner plugin implementation of A*, JPS, D*, LPA*, D* Lite, Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, PSO, Voronoi, PID, LQR, MPC, DWA, APF, Pure Pursuit etc.
Learn the basics of robotics through hands-on experience using ROS 2 and Gazebo simulation.
Python implementation of a bunch of multi-robot path-planning algorithms.
An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)
3D Trajectory Planner in Unknown Environments
The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
Quadrotor control, path planning and trajectory optimization
Python sample codes and documents about Autonomous vehicle control algorithm. This project can be used as a technical guide book to study the algorithms and the software architectures for beginners.
Motion planning(Path Planning and Trajectory Planning/Tracking) of AGV/AMR：python implementation of Dijkstra, A*, JPS, D*, LPA*, D* Lite, (Lazy)Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, Voronoi, PID, DWA, APF, LQR, MPC, RPP, Bezier, Dubins etc.
[CMU] A Versatile and Modular Framework Designed for Autonomous Unmanned Aerial Vehicles [UAVs] (C++/ROS/PX4)
Robust and efficient coverage paths for autonomous agricultural vehicles. A modular and extensible Coverage Path Planning library
灰狼优化算法(GWO)路径规划/轨迹规划/轨迹优化、多智能体/多无人机航迹规划
The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations.
Trajectory Planner in Multi-Agent and Dynamic Environments
Optimization-based real-time path planning for vehicles.
Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
深度强化学习路径规划, SAC-Auto路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真模拟，Adaptive-SAC
Add a description, image, and links to the path-planning topic page so that developers can more easily learn about it.
To associate your repository with the path-planning topic, visit your repo's landing page and select "manage topics."