Symmetry Breaking for k-Robust Multi-Agent Path Finding

TL;DR

This paper introduces symmetry breaking constraints tailored for k-robust multi-agent pathfinding, significantly improving success rates in various benchmark and real-world domains by efficiently resolving conflicts between agents.

Contribution

The work presents novel pairwise symmetry breaking constraints specifically designed for k-robust MAPF, enhancing the efficiency and success rate of finding optimal, conflict-free paths.

Findings

Large improvements in success rate across multiple benchmarks

Effective application to automated warehouse and railway scheduling domains

Enhanced robustness of multi-agent plans against delays

Abstract

During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events. To address such situations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and collision-free plan that is robust for up to k delays. In this work we introducing a variety of pairwise symmetry breaking constraints, specific to k-robust planning, that can efficiently find compatible and optimal paths for pairs of conflicting agents. We give a thorough description of the new constraints and report large improvements to success rate ina range of domains including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and; (iii) on maps from the 2019 Flatland Challenge, a recently introduced railway domain where k-robust planning can be fruitfully applied to schedule trains.