用局部引导提升多智能体路径规划效率，不增加计算负担
Local Guidance for Configuration-Based Multi-Agent Pathfinding
- 在每个智能体附近提供局部时空指引，替代全局信息
- 在不超时的前提下，显著提升路径规划质量
- 适合需要实时响应的多智能体系统应用
引导（Guidance）是提升实时、次优多智能体路径规划（MAPF）方法性能的新兴概念。它通过考虑整个工作空间中所有智能体的集体行为，提供全局视角以缓解拥堵，降低等待时间，提升整体协调效率。本文提出一种替代方案：为每个智能体周边提供局部引导。尽管这类方法在智能体移动时需重新计算，可能显得计算量大，但实验表明，向规划器提供有信息量的时空提示，可在适度时间预算内显著改善解的质量。该方法应用于领先的基于配置的求解器LaCAM，建立了新的性能边界。
原文摘要 · Abstract (English)
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.
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