Adaptive Lazy Collision Checking for Optimal Sampling-based Motion Planning

Abstract

Lazy collision checking has been proposed to reduce the computational burden of collision checking which is considered as a major computational bottleneck in sampling-based motion planning. Unfortunately, in complex environments with many obstacles, lazy collision checking can cause an excessive amount of optimistic thrashing problems and significantly degrade the performance of the planner. In this paper, we present an adaptive lazy collision checking method to alleviate the optimistic thrashing, thus broaden the applicability. Our method delays collision checking on the regions predicted to be in the configuration free space, while checking early on the other regions to reduce the optimistic thrashing. To identify such regions, we adopt a configuration free space approximation represented by a set of hyperspheres which can be constructed without significant proximity computation. To demonstrate benefits of our approach we have compared against prior methods including RRT*, PRM* and lazy PRM* across benchmarks with varying dimensions. Overall, our method shows meaningful performance improvement up to three times higher in terms of convergence speed over the tested methods. We also discuss properties and theoretical analysis of the proposed algorithm.