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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 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.
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
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.
深度强化学习路径规划, SAC路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真，Adaptive-SAC
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
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