# Motion Planning

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.

Repository: https://github.com/ai-winter/python_motion_planning
Canonical: https://ross.abutalabs.com/products/motion-planning
Homepage: https://ai-winter.github.io/python_motion_planning/
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: a-star, d-star, d-star-lite, dijkstra, informed-rrt-star, jump-point-search, motion-planning, rrt, rrt-connect, rrt-star, voronoi, bezier-curve, dubins-curve, theta-star, artificial-potential-field, model-predictive-control, autonomous-vehicles, path-planning, path-tracking, trajectory-planning
Last push: 2026-06-02T09:04:52+00:00
Link (homepage): https://ai-winter.github.io/python_motion_planning/

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 85, release rhythm 74, longevity 93
- inputs: {"age_days": 1303, "days_push": 92, "days_rel": 92, "gap_med": 66.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1074, forks 149 (observed 2026-08-28T04:03:29.093057+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: simulation, robotics
- domain: robotics, autonomous-vehicles, education
- platform: python, cpp, cross-platform
- tags: 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
- ai-winter/python_motion_planning (main) score 83
- ai-winter/ros_motion_planning (plugin) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:29.093057+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:33:57.345484+00:00, confidence not recorded.
  - readme: https://github.com/ai-winter/python_motion_planning (fetched 2026-08-28T04:03:29.093057+00:00, sha fcd21e688de8)
  - homepage: https://ai-winter.github.io/python_motion_planning/ (fetched 2026-08-29T09:27:26.728510+00:00, sha 36c6c3c2e4f9)
  - registry_pypi: https://pypi.org/pypi/python_motion_planning/json (fetched 2026-08-29T09:27:26.731477+00:00, sha 530d3045c1b4)
- Data as of 2026-08-30T08:39:29.467469+00:00.