OMPL 2.0升级为实时运动规划工具，支持硬件加速与现代AI工作流。
The Open Motion Planning Library 2.0
- 采用硬件加速提升实时规划性能，支持新算法与约束条件。
- 整合时序逻辑目标与渐近最优规划，扩展应用场景。
- 适合机器人研究者及需要高效规划的AI/ML开发者。
Open Motion Planning Library（OMPL）自2008年发布以来，已成为运动规划领域的基石，提供大量先进采样式算法实现。近二十年持续开发中，新增了渐近最优、懒惰规划器、带约束运动规划及带时序逻辑目标的规划等能力。在此基础上，本文推出OMPL 2.0，实现重大演进：通过硬件加速支持实时运动规划，并无缝集成现代AI研究工作流。同时回顾了OMPL与运动规划领域共同发展的历程，探讨其对科研社区的广泛影响。
原文摘要 · Abstract (English)
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.
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