While the study of unit-cost Multi-Agent Pathfinding (MAPF) problems has been popular, many real-world problems require continuous time and costs due to various movement models. In this context, this paper studies symmetry-breaking enhancements for Continuous-Time Conflict-Based Search (CCBS), a solver for continuous-time MAPF. Resolving conflict symmetries in MAPF can require an exponential amount of work. We adapt known enhancements from unit-cost domains for CCBS: bypassing, which resolves cost symmetries and biclique constraints which resolve spatial conflict symmetries. We formulate a novel combination of biclique constraints with disjoint splitting for spatial conflict symmetries. Finally, we show empirically that these enhancements yield a statistically significant performance improvement versus previous state of the art, solving problems for up to 10% or 20% more agents in the same amount of time on dense graphs.

翻译：尽管单位成本多智能体路径规划问题的研究已十分流行，但现实世界中的许多问题因运动模型差异而需要连续时间与连续成本。在此背景下，本文研究了连续时间冲突搜索（一种面向连续时间多智能体路径规划的求解器）的对称性破缺增强技术。解决多智能体路径规划中的冲突对称性需要指数级的工作量。我们将单位成本领域已知的增强技术适配至连续时间冲突搜索：解决成本对称性的"绕过"策略，以及解决空间冲突对称性的双团约束。我们创新性地提出了双团约束与不相交分裂相结合的方法，用于处理空间冲突对称性。实验结果表明，这些增强技术相较于先前最优方法带来了统计显著的性能提升，在稠密图上相同时间内可多解决10%至20%的智能体路径规划问题。