Motion Planning

Health v2 · maintenance only

Adoption not part of the score

1074 stars · 149 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of motion planning algorithm implementations covering graph search (A*, JPS, D*, LPA*, D* Lite, Theta*), sampling-based planning (RRT variants, ACO, PSO, Voronoi), and trajectory optimization/tracking (PID, LQR, MPC, DWA, APF, Pure Pursuit). Available as a ROS planner plugin in C++ and as a standalone Python library for learning and prototyping.

Use cases

• implement path planning for a mobile robot
• compare A* vs RRT vs D* Lite algorithms
• learn motion planning algorithms with visual demos
• add a custom local planner plugin to ROS move_base
• prototype trajectory tracking controllers like MPC and Pure Pursuit
• simulate AGV/AMR navigation in ROS and Gazebo
• study path searching and trajectory optimization theory

When to choose

• you need reference implementations of many classic motion planning algorithms in one codebase
• you are building ROS-based robot navigation with planner plugins
• you want runnable, visualizable code to learn planning algorithms
• you are prototyping AGV/AMR navigation stacks

When to avoid

• you need production-grade, safety-certified navigation for commercial deployment
• you work outside ROS/Python and need a turnkey planner binary
• you need 3D planning for aerial or manipulator motion out of the box
• you require permissive licensing - the code is GPL-3.0

Facets

library · maturity active

simulation robotics robotics autonomous-vehicles education python cpp cross-platform motion-planning path-planning trajectory-optimization astar rrt dwa mpc ros-navigation agv amr path-tracking sampling-based-planning algorithm-visualization algorithms linux ros

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ai-winter/python_motion_planning")