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OMPL 2.0 introduces hardware acceleration for real-time motion planning and integrates with modern AI research workflows, marking a significant evolution of this widely-used motion planning library. The update incorporates new planners, state spaces, and problem formulations developed over two decades, including asymptotically optimal, lazy, and constrained motion planning algorithms. This release aims to enhance the library's utility for real-time applications and its compatibility with contemporary AI research practices.
Motion planning just got a whole lot faster: OMPL 2.0 brings hardware acceleration to your robot's pathfinding.
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.
