Faster Motion Planning via Restarts

TL;DR

This paper introduces stochastic restart techniques to significantly accelerate randomized motion planning algorithms like PRM and RRT, resulting in faster runtimes, shorter paths, and improved multi-threading efficiency.

Contribution

The paper presents new stochastic restart algorithms for Las Vegas motion planning methods, proving their optimality and demonstrating substantial practical speedups.

Findings

Achieved up to 3x speedup in motion planning tasks

Generated shorter paths compared to traditional methods

Enhanced multi-threading performance in experiments

Abstract

Randomized methods such as PRM and RRT are widely used in motion planning. However, in some cases, their running-time suffers from inherent instability, leading to ``catastrophic'' performance even for relatively simple instances. We apply stochastic restart techniques, some of them new, for speeding up Las Vegas algorithms, that provide dramatic speedups in practice (a factor of $3$ [or larger] in many cases). Our experiments demonstrate that the new algorithms have faster runtimes, shorter paths, and greater gains from multi-threading (when compared with straightforward parallel implementation). We prove the optimality of the new variants. Our implementation is open source, available on github, and is easy to deploy and use.