Reasoning and Planning

OMPL

The Open Motion Planning Library provides sampling-based motion planners for robots, vehicles, and high-dimensional configuration spaces.

Tool Introduction

Core parameters, trigger timing, and visual before/after demo references.

Short Explanation

Use OMPL when execution needs a collision-free motion plan through a robot configuration space.

InputState space, validity checker, start and goal states

OutputCollision-free path or planning failure

Trigger TimingTriggered on demand after the required input files and configuration are prepared.

RuntimeC++ / Python bindings / MoveIt integration

BeforeState space, validity checker, start and goal states

Prepare the scene, image, video, sensor stream, prompt, or configuration expected by the original project.

AfterCollision-free path or planning failure

Read the produced visualization, prediction, map, trajectory, mask, grasp pose, or other documented artifact.

Parameters And Output

Readable controls and the meaning of each returned artifact.

Parameter Explanation

state_spaceselect

Configuration-space representation used by the planner.

validity_checkerpath

Function that rejects states in collision or outside constraints.

start_goaltext

Initial and target states for the planning problem.

Output Explanation

solution_path

Collision-free path returned by the planner.

planner_status

Solved, approximate, timeout, or failure status.

How To Use

Official resources, deployment steps, academic context, citation, and source-reported benchmark numbers.

Resources

GitHubhttps://github.com/ompl/ompl Code Downloadhttps://github.com/ompl/ompl/archive/refs/heads/main.zip Websitehttps://ompl.kavrakilab.org/

Deployment Notes

Install OMPL directly or through robotics frameworks such as MoveIt.
Define the state space and collision/validity checker for the robot and scene.
Choose a planner and solve within the allotted planning time.

Relative Path Example

# Define an OMPL state space, state validity checker, start and goal states, then solve with a planner such as RRTConnect or PRM.

Expected Result Shape

{
  "tool": "ompl",
  "status": "ok",
  "results": [
    {
      "label": "Motion planning",
      "score": 0.87,
      "output": "Collision-free path or planning failure"
    }
  ],
  "timing": {
    "runtime": "Deployment-specific; use OMPL benchmark logs for planning time, success rate, solution length, and path quality on the target planning problem.",
    "device": "documented in source benchmark when available"
  },
  "artifacts": {
    "visualization": "tools/ompl/runs/visualization.png",
    "raw_predictions": "tools/ompl/runs/predictions.json"
  }
}

Paper figure

Academic Info

Paper identity and contribution summary.

TitleThe Open Motion Planning Library

AuthorsAdd authors

VenueIEEE Robotics & Automation Magazine 2012

ContributionProvides a reusable library of sampling-based planners and state-space abstractions for robot motion planning.

Citation

@misc{ompl2012,
  title={The Open Motion Planning Library},
  author={Author},
  year={2012},
  note={IEEE Robotics & Automation Magazine 2012},
  url={https://github.com/ompl/ompl}
}

Benchmark

Only compact, source-reported numbers are shown here.

Dataset	Metric	Value	Runtime	Source
OMPL ships benchmarking infrastructure and Planner Arena-style comparison workflows, but the library does not expose one canonical official benchmark number for all planners and problem classes.	Core result	No single numeric score is copied here because OMPL performance depends on the selected planner, state space, validity checker, robot geometry, and timeout.	Deployment-specific; use OMPL benchmark logs for planning time, success rate, solution length, and path quality on the target planning problem.	IEEE Robotics & Automation Magazine 2012

Artifacts

Official OMPL docs, benchmark database/logs, Planner Arena reports, planner configuration, and solved path artifacts.

Demo Images

Visual references from the original tool. Click any image to inspect the original size.