The Provable Virtue of Laziness in Motion Planning

The Provable Virtue of Laziness in Motion Planning

Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha Srinivasa

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 6161-6165. https://doi.org/10.24963/ijcai.2019/855

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along candidate shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm such as manipulation in cluttered environments and planning for robots in surgical settings; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.
Keywords:
Robotics: Motion and Path Planning
Planning and Scheduling: Robot Planning
Planning and Scheduling: Theoretical Foundations of Planning
Planning and Scheduling: Planning Algorithms