Ross ROSS = Recommend OSS · open-source software intelligence for agents

ompl/ompl

The Open Motion Planning Library (OMPL) observed · 2026-08-28

github.com/ompl/ompl · homepage · C++ · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

95/100

  • Activity 99
  • Release rhythm 86
  • Longevity 100

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 73
  • age_days: 4488
  • days_rel: 19
  • days_push: 7
  • n_releases_24m: 4

Full methodology

Adoption not part of the score

2134 stars · 706 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity stable

simulation machine-learning benchmarking robotics autonomous-vehicles simulation windows cpp python cross-platform motion-planning sampling-based-planning rrt prm robotics vamp moveit collision-avoidance kinodynamic-planning algorithms linux macos

6 sources

Member repositories

RepositoryRoleHealth v2
ompl/omplmain95

For agents

markdown · JSON · MCP: product_card(name="ompl/ompl")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem