The Open Motion Planning Library 2.0

2026-05-27

AI Summary

This work addresses the limitations of traditional sampling-based motion planning algorithms in real-time performance and integration with modern AI research workflows by presenting a systematic upgrade to the open-source OMPL library. For the first time, hardware acceleration support—encompassing GPUs and FPGAs—is introduced into OMPL, alongside enhanced compatibility with mainstream AI toolchains. The extension supports diverse planning paradigms, including asymptotically optimal planning, lazy sampling, constraint handling, and task specifications expressed in linear temporal logic. These advancements substantially improve computational efficiency and scalability, reinforcing OMPL’s foundational role in motion planning while significantly broadening its applicability to complex intelligent systems and autonomous robotic platforms.

Abstract

The Open Motion Planning Library (OMPL), first released in 2008, has become a cornerstone of the motion planning community, providing implementations of a wide range of state-of-the-art sampling-based algorithms. Over almost two decades of continuous development, we have steadily expanded the library with new planners, state spaces, and problem formulations. These additions range from asymptotically optimal and lazy planners to constrained motion planning and planning with temporal-logic goals. Building on this foundation, we introduce OMPL 2.0, a major evolution of the library that targets real-time motion planning through hardware acceleration and integrates seamlessly with modern AI research workflows. We also reflect on how OMPL and the field of motion planning have grown together over the years, and discuss the library's broader impact on the research community.

Problem

• motion planning
• real-time planning
• hardware acceleration
• AI integration
• open-source library

Innovation

• real-time motion planning
• hardware acceleration
• AI research workflows
• constrained motion planning
• temporal-logic goals