Symmetry Breaking for k-Robust Multi-Agent Path Finding
This work addresses robustness in path planning for agents like robots or trains, but it is incremental as it builds on existing k-Robust Conflict-Based Search methods.
The paper tackled the problem of unexpected delays in Multi-Agent Path Finding (MAPF) by introducing pairwise symmetry breaking constraints for k-robust planning, resulting in large improvements to success rate across various domains.
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.