The Open Motion Planning Library 2.0
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, OMPL 2.0 targets real-time motion planning through hardware acceleration and integrates seamlessly with modern AI research workflows.
Article intelligence
Key points
- OMPL 2.0 is a major upgrade focusing on real-time motion planning and hardware acceleration.
- The new version integrates with modern AI research tools for more efficient workflows.
- OMPL has grown over nearly two decades, incorporating many new planners and state spaces.
- The library has been instrumental in advancing the field of motion planning.
Why it matters
This matters because OMPL 2.0 is a major upgrade focusing on real-time motion planning and hardware acceleration.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.29301] The Open Motion Planning Library 2.0
[Submitted on 28 May 2026]
Title:The Open Motion Planning Library 2.0
View a PDF of the paper titled The Open Motion Planning Library 2.0, by Weihang Guo and 7 other authors
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.29301 [cs.RO]
(or arXiv:2605.29301v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29301
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Weihang Guo [view email] [v1] Thu, 28 May 2026 03:32:28 UTC (604 KB)
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