Guidance is an emerging concept that improves the empirical performance of real-time, sub-optimal multi-agent pathfinding (MAPF) methods. It offers additional information to MAPF algorithms to mitigate congestion on a global scale by considering the collective behavior of all agents across the entire workspace. This global perspective helps reduce agents' waiting times, thereby improving overall coordination efficiency. In contrast, this study explores an alternative approach: providing local guidance in the vicinity of each agent. While such localized methods involve recomputation as agents move and may appear computationally demanding, we empirically demonstrate that supplying informative spatiotemporal cues to the planner can significantly improve solution quality without exceeding a moderate time budget. When applied to LaCAM, a leading configuration-based solver, this form of guidance establishes a new performance frontier for MAPF.

翻译：引导是一种新兴概念，旨在提升实时次优多智能体路径规划（MAPF）方法的实证性能。它通过考虑所有智能体在整个工作空间中的集体行为，为MAPF算法提供额外信息，以在全局范围内缓解拥堵。这种全局视角有助于减少智能体的等待时间，从而提高整体协调效率。相比之下，本研究探索了一种替代方案：在每个智能体附近提供局部引导。尽管此类局部化方法涉及智能体移动时的重新计算，且可能显得计算量较大，但我们通过实证表明，为规划器提供具有信息量的时空线索，可以在不超过适度时间预算的情况下显著提升求解质量。当应用于领先的基于配置的求解器LaCAM时，这种引导形式为MAPF建立了新的性能前沿。