Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. Typically low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. In particular, one of these variants can obtain large speedups, 2-100x, across several scenarios and suboptimal CBS methods. Importantly, we discover that performance is related not to the weighted cost-to-go heuristic but rather to the relative conflict heuristic weight's ability to effectively balance low-level and high-level work. Additionally, to the best of our knowledge, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization. Update March 2024: We found that the relative speedup decreases to around 1.2-10x depending on how the conflict heuristic is computed (see appendix for more details).

翻译：冲突基搜索（CBS）是一种流行的多智能体路径规划（MAPF）求解器，它采用底层单智能体规划器和顶层约束树来解决冲突。当前绝大多数MAPF求解器通过多种策略减小约束树规模来改进CBS，但鲜有方法修改底层规划器。现有CBS方法中的底层规划器通常使用未加权的成本目标启发式，次优CBS方法则额外采用冲突启发式辅助顶层搜索。本文证明，与现有CBS主流认知相反，加权成本目标启发式可与冲突启发式在两种变体中有效协同工作。特别地，其中一种变体在多种场景和次优CBS方法下可实现2-100倍的显著加速。重要的是，我们发现性能提升并非源于加权成本目标启发式本身，而是取决于相对冲突启发式权重有效平衡底层与顶层工作的能力。此外，据我们所知，本文首次建立了优先级规划与有界次优CBS之间的理论关联，并证明我们的方法为其自然推广。2024年3月更新：我们发现相对加速比会根据冲突启发式计算方式降至约1.2-10倍（详见附录）。