# zhm-real/MotionPlanning

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

Repository: https://github.com/zhm-real/MotionPlanning
Canonical: https://ross.abutalabs.com/products/motionplanning
Homepage: https://github.com/zhm-real/MotionPlanning
Language: Python
License Family: other
Topics: hybrid-astar, state-lattice-planner, pure-pursuit, rear-wheel-feedback, front-wheel-feedback, stanley-controller, mpc, autonomous-vehicles, stanley, lqr, frenet-frames, tracking, pathplanning, motion-planning-algorithms, path-tracking
Last push: 2024-01-13T06:05:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2247, "days_push": 963, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2734, forks 634 (observed 2026-08-28T04:07:16.225878+00:00)

## What it is
A Python collection of motion planning and path tracking algorithms for autonomous vehicles, including Hybrid A*, Frenet Optimal Trajectory, State Lattice planners, and controllers like Pure Pursuit, Stanley, LQR, and MPC. It serves as an educational reference implementation with visual demos rather than production software.

## Use cases
- implement hybrid A* path planning for autonomous vehicles
- learn path tracking controllers like pure pursuit and Stanley
- generate Frenet frame optimal trajectories
- simulate car and trailer vehicle models in Python
- study motion planning algorithms for self-driving cars
- compare LQR vs MPC steering controllers

## When to choose
- learning or prototyping autonomous vehicle planning and control algorithms
- you want readable Python reference implementations with visualizations
- research or coursework on path planning and tracking

## When to avoid
- production autonomous driving systems needing safety certification
- you need a maintained, licensed package (no license is provided)
- real-time embedded deployment

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, machine-learning
- domain: autonomous-vehicles, robotics, simulation
- platform: python, cross-platform
- tags: motion-planning, path-planning, path-tracking, hybrid-astar, frenet-trajectory, pure-pursuit, stanley-controller, lqr, mpc, autonomous-driving, vehicle-models, algorithm-visualization, algorithms

## Member repositories
- zhm-real/MotionPlanning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:16.225878+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 2026-08-30T02:14:29.044818+00:00, confidence not recorded.
  - readme: https://github.com/zhm-real/MotionPlanning (fetched 2026-08-28T04:07:16.225878+00:00, sha 15f31e71bdbb)
  - homepage: https://github.com/zhm-real/MotionPlanning (fetched 2026-08-29T09:57:34.437622+00:00, sha 616910d90e42)
- Data as of 2026-08-30T08:39:29.467469+00:00.