On the Use of Randomized Low-Discrepancy Sequences in Sampling-Based Motion Planning

摘要

This paper shows the performance of randomized low-discrepancy sequences compared with others low-discrepancy sequences. We used two motion planning algorithms to test this performance: the expansive planner proposed in [1], [2] and SBL. Previous research already showed that the use of deterministic sampling outperformed PRM approaches. Experimental results show performance advantages when we use randomized Halton and Sobol sequences over Mersenne-Twister and the linear congruential generators used in random sampling.