# ompl/ompl

The Open Motion Planning Library (OMPL)

Repository: https://github.com/ompl/ompl
Canonical: https://ross.abutalabs.com/products/ompl
Homepage: https://ompl.kavrakilab.org
Language: C++
License: NOASSERTION
License Family: other
Topics: motion-planning, robotics
Last push: 2026-08-26T03:02:37+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 4488, "days_push": 7, "days_rel": 19, "gap_med": 73, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2134, forks 706 (observed 2026-08-28T04:06:17.461364+00:00)

## What it is
OMPL is an open-source C++ library of over 40 sampling-based motion planning algorithms (RRT-Connect, PRM, RRT*, KPIECE, etc.) across more than 20 state spaces, with Python bindings via Nanobind. It is deliberately decoupled from collision checking and visualization, making it the default planning backend in ROS/MoveIt and integrable into many robot software systems.

## Use cases
- plan collision-free paths for robot arms
- compute motion plans for mobile robots
- plan paths for kinematically constrained manipulators
- benchmark and compare motion planning algorithms
- generate motion planning datasets for learning-based planners
- rapid millisecond replanning with SIMD-accelerated collision checking
- integrate motion planning into ROS/MoveIt pipelines
- prototype custom planners and state spaces in Python or C++

## When to choose
- you need sampling-based motion planning for robots in C++ or Python
- you want a mature, well-tested planner library that integrates with ROS/MoveIt
- you need many planner algorithms and state spaces to experiment with or benchmark
- you need high-performance planning via the VAMP SIMD backend
- you are doing research on task and motion planning or learning for planning

## When to avoid
- you need built-in collision checking, visualization, or robot kinematics (OMPL expects you to supply these)
- you need optimal control or trajectory optimization rather than sampling-based planning
- you need a turnkey robotics stack without ROS or simulator integration
- your project requires a permissive license without review (license is custom/non-standard)

## Facets
- artifact type: library
- maturity: stable
- function: simulation, machine-learning, benchmarking
- domain: robotics, autonomous-vehicles, simulation
- platform: windows, cpp, python, cross-platform
- tags: motion-planning, sampling-based-planning, rrt, prm, robotics, vamp, moveit, collision-avoidance, kinodynamic-planning, algorithms, linux, macos

## Member repositories
- ompl/ompl (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:17.461364+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 2026-08-30T02:51:57.089654+00:00, confidence not recorded.
  - readme: https://github.com/ompl/ompl (fetched 2026-08-28T04:06:17.461364+00:00, sha fa7dc3d0bcd6)
  - homepage: https://ompl.kavrakilab.org (fetched 2026-08-29T10:32:02.816886+00:00, sha ef71f884b3cd)
  - site_page: https://ompl.kavrakilab.org/installation.html (fetched 2026-08-29T10:32:02.819576+00:00, sha 80e24c33cf16)
  - site_page: https://ompl.kavrakilab.org/FAQ.html (fetched 2026-08-29T10:32:02.821341+00:00, sha d9d7a9a0e2f2)
  - site_page: https://ompl.kavrakilab.org/integration.html (fetched 2026-08-29T10:32:02.823109+00:00, sha 13d4e14018a3)
  - site_page: https://ompl.kavrakilab.org/python.html (fetched 2026-08-29T10:32:02.824910+00:00, sha 3eb6a68390ef)
- Data as of 2026-08-30T08:39:29.467469+00:00.