https://github.com/hiroishida/ompl-thin-python
A thin python-binding of OMPL motion planning library (*For my own research)
https://github.com/hiroishida/ompl-thin-python
Last synced: about 1 month ago
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A thin python-binding of OMPL motion planning library (*For my own research)
- Host: GitHub
- URL: https://github.com/hiroishida/ompl-thin-python
- Owner: HiroIshida
- License: mit
- Created: 2022-12-11T11:18:52.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-11-02T08:42:07.000Z (over 1 year ago)
- Last Synced: 2025-03-21T12:50:14.556Z (about 1 year ago)
- Language: C++
- Homepage:
- Size: 95.7 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
## ompl-thin [](https://pypi.org/project/ompl-thin/)
If you are looking for a thicker wrapper with more extensive features, please refer to OMPL's [official python bindings](https://ompl.kavrakilab.org/python.html).
The main advantages of this package are fast build times and simplicity.
This is achieved by focusing solely on thin-wrapping the OMPL library and leveraging pybind11.
If your primary use case for OMPL involves articulated robots, we recommend trying [plainmp](https://github.com/HiroIshida/plainmp).
## installation
The package is available on [pypi](https://pypi.org/project/ompl-thin/). So, to install you just need to
```bash
pip3 install ompl-thin
```
NOTE: binary distribution is available only for x86_64. For other architecture, only source distributions are available and takes few minutes to install. Also, before installation, you need to run the following to get shared dependency.
```
sudo apt install libboost-all-dev -y
```
## Usage
please try
```bash
python example/many_algorithms.py
```