Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning

TL;DR

This paper introduces discrete-RRT, a novel pathfinding algorithm for multi-robot motion planning that efficiently explores implicit high-dimensional roadmaps, significantly outperforming existing methods in speed.

Contribution

It presents a new sampling-based framework combining implicit roadmap representation with discrete-RRT for efficient multi-robot pathfinding.

Findings

Algorithm is at least ten times faster than existing methods.

Successfully applied to scenarios with up to 60 degrees of freedom.

Demonstrates effective exploration of high-dimensional configuration spaces.

Abstract

We present a sampling-based framework for multi-robot motion planning which combines an implicit representation of a roadmap with a novel approach for pathfinding in geometrically embedded graphs tailored for our setting. Our pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated RRT algorithm for the discrete case of a graph, and it enables a rapid exploration of the high-dimensional configuration space by carefully walking through an implicit representation of a tensor product of roadmaps for the individual robots. We demonstrate our approach experimentally on scenarios of up to 60 degrees of freedom where our algorithm is faster by a factor of at least ten when compared to existing algorithms that we are aware of.