# ompl

**Repository Path**: maqun001/ompl

## Basic Information

- **Project Name**: ompl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-06-17
- **Last Updated**: 2026-06-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

The Open Motion Planning Library (OMPL)
=======================================

**OMPL** is an open source sampling-based motion planning library

- Over 40 sampling-based planning algorithms (RRT-Connect, PRM, KPIECE, RRT*, and **many more**) across more than 20 state spaces (SE(3), Euclidean space, and others)
- Easily extensible to custom planners (Python and C++) and state spaces (C++)
- SIMD-accelerated planning with VAMP for **millisecond planning** in both Python and C++

[![Build](https://github.com/ompl/ompl/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/ompl/ompl/actions/workflows/build.yml)
[![Format](https://github.com/ompl/ompl/actions/workflows/format.yml/badge.svg?branch=main)](https://github.com/ompl/ompl/actions/workflows/format.yml)
[![PyPI](https://img.shields.io/pypi/v/ompl?color=orange)](https://pypi.org/project/ompl/)

[![Paper](https://img.shields.io/badge/RA--M-OMPL-blue)](https://dx.doi.org/10.1109/MRA.2012.2205651)
[![Paper](https://img.shields.io/badge/ICRA-OMPL_Python-blue)](https://arxiv.org/abs/2603.04668)
[![Paper](https://img.shields.io/badge/RA--M-Benchmarking-blue)](https://dx.doi.org/10.1109/MRA.2015.2448276)
[![Paper](https://img.shields.io/badge/IJRR-Constrained_Planning-blue)](https://dx.doi.org/10.1177/0278364919868530)
[![Paper](https://img.shields.io/badge/ICRA-VAMP-blue)](https://dx.doi.org/10.1109/ICRA57147.2024.10611190)

Installation
------------

Visit the [OMPL installation page](https://ompl.kavrakilab.org/core/installation.html) for
detailed installation instructions.

OMPL has the following required dependencies:

* [Boost](https://www.boost.org) (version 1.68 or higher)
* [CMake](https://www.cmake.org) (version 3.12 or higher)
* [Eigen](http://eigen.tuxfamily.org) (version 3.3 or higher)

The following dependencies are optional:

* [**VAMP**](https://github.com/KavrakiLab/vamp) (enabled by default) - Vector-Accelerated Motion Planning for high-performance collision checking with SIMD optimization
* [Doxygen](http://www.doxygen.org) (needed to create a local copy of the documentation at
  https://ompl.kavrakilab.org/core)
* [Flann](https://github.com/flann-lib/flann/tree/1.9.2) (FLANN can be used for nearest neighbor queries by OMPL)
* [Spot](http://spot.lrde.epita.fr) (Used for constructing finite automata from LTL formulae.)
* [yaml-cpp](https://github.com/jbeder/yaml-cpp) (Used for reading and writing YAML world descriptions in the PlanarManipulator demos)

Once dependencies are installed, you can build OMPL on Linux, macOS,
and MS Windows. Go to the top-level directory of OMPL and type the
following commands:

    git submodule update --init --recursive
    mkdir -p build/Release
    cd build/Release
    cmake ../..
    make -j <num_cores> # replace <num_cores> with the number of cores on your machine

To install the Python bindings, go to the top-level directory of OMPL and type the following commands:
```
git submodule update --init --recursive
pip install ./py-bindings
```