pyompl 0.1.2

Python bindings for the Open Motion Planning Library (OMPL)

pyompl

pyompl provides Python bindings for the Open Motion Planning Library (OMPL), enabling efficient motion planning algorithms to be used directly from Python.

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What is OMPL?

The Open Motion Planning Library (OMPL) is a widely used C++ library for solving motion planning problems, particularly in robotics. It implements a variety of state-of-the-art sampling-based algorithms such as:

• RRT (Rapidly-exploring Random Trees)
• PRM (Probabilistic Roadmaps)
• RRT*
• BIT*

OMPL is designed to be:

• Efficient — optimized for high-performance planning in complex spaces
• Flexible — supports different state spaces and constraints
• Modular — easy to extend with custom planners and components

It is commonly used in robotics, autonomous systems, and simulation environments.

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Why is OMPL useful?

Motion planning is a core problem in robotics and automation: determining a valid path from a start state to a goal state while avoiding obstacles.

OMPL provides:

• Robust implementations of proven planning algorithms
• Scalability to high-dimensional spaces
• Abstraction layers for different types of planning problems

Instead of implementing these algorithms from scratch, developers can rely on OMPL’s well-tested and optimized implementations.

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What is Python?

Python is a high-level, general-purpose programming language known for:

• Simple and readable syntax
• Rapid prototyping capabilities
• Extensive ecosystem of scientific and machine learning libraries

It is widely used in:

• Robotics research
• Data science and machine learning
• Simulation and experimentation

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Why Python bindings for OMPL?

While OMPL is written in C++ for performance, many researchers and developers prefer Python for:

• Faster experimentation
• Easier integration with tools like NumPy, SciPy, and ML frameworks
• Reduced development overhead

By exposing OMPL to Python, we combine:

• C++ performance
• Python usability

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Why use pybind11?

pybind11 is a lightweight library that enables seamless interoperability between C++ and Python.

It allows us to:

• Expose C++ classes and functions to Python
• Maintain near-native performance
• Write minimal “glue code”

Using pybind11, pyompl wraps OMPL’s core functionality and makes it accessible as a Python module.

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Summary

pyompl bridges the gap between high-performance motion planning in C++ and the ease of use of Python, making advanced planning algorithms more accessible for research, prototyping, and application development.