Spatial and Temporal Splitting Heuristics for Multi-Robot Motion Planning

TL;DR

This paper introduces spatial and temporal splitting heuristics for multi-robot motion planning, significantly improving scalability and efficiency of existing algorithms in complex environments.

Contribution

It proposes novel spatio-temporal splitting schemes that enhance existing MRMP algorithms, enabling solutions to larger problems with minimal optimality loss.

Findings

Solved problems 10+ times larger than previous methods

Increased ECBS scalability by 5-15 times on challenging maps

Maintained near-optimal solutions with effective heuristics

Abstract

In this work, we systematically examine the application of spatio-temporal splitting heuristics to the Multi-Robot Motion Planning (MRMP) problem in a graph-theoretic setting: a problem known to be NP-hard to optimally solve. Following the divide-and-conquer principle, we design multiple spatial and temporal splitting schemes that can be applied to any existing MRMP algorithm, including integer programming solvers and Enhanced Conflict Based Search, in an orthogonal manner. The combination of a good baseline MRMP algorithm with a proper splitting heuristic proves highly effective, allowing the resolution of problems 10+ times than what is possible previously, as corroborated by extensive numerical evaluations. Notably, spatial partition of problem fusing with the temporal splitting heuristic and the enhanced conflict based search (ECBS) algorithm increases the scalability of ECBS on large and challenging DAO maps by 5--15 folds with negligible impact on solution optimality.