Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

TL;DR

This paper introduces a hierarchical approach to multi-robot motion planning that significantly reduces computation time by iteratively refining workspace decomposition to enable more decoupled robot planning.

Contribution

It proposes a novel hierarchical subproblem expansion method that refines workspace decomposition for more efficient multi-robot motion planning.

Findings

Planning time improved up to tenfold.

Hierarchical refinement enables smaller, decoupled configuration spaces.

Method effectively reduces computational complexity in multi-robot scenarios.

Abstract

A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.