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.

本文介绍了多机器人路径规划问题中考虑异常情况的 k-Robust Conflict-Based Search 算法，以及引入对 k-robust 规划特定的对称性约束可以有效地找到冲突代理的兼容和最优路径，在多个领域中的应用效果较好，包括传统的 MAPF 基准测试，自动化仓库领域和最近推出 Flatland 挑战的铁路领域。