The Open Motion Planning Library 2.0
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.
Summary¶
Describes OMPL 2.0 as a major update to the long-running Open Motion Planning Library, adding modern planning abstractions, planners, and benchmarking support. The paper is an infrastructure reference for sampling-based motion planning rather than a single-planner algorithm paper.