Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan Graph provides a framework for finding an acyclic Temporal Plan Graph with the minimum execution cost under delays, ensuring deadlock- and collision-free execution. Unfortunately, existing optimal algorithms, such as Mixed Integer Linear Programming and Graph-Based Switchable Edge Search (GSES), are often too slow for practical use. This paper introduces Improved GSES, which significantly accelerates GSES through four speedup techniques: stronger admissible heuristics, edge grouping, prioritized branching, and incremental implementation. Experiments conducted on four different map types with varying numbers of agents demonstrate that Improved GSES consistently achieves over twice the success rate of GSES and delivers up to a 30-fold speedup on instances where both methods successfully find solutions.

翻译：多智能体路径规划（MAPF）旨在为多个智能体规划无碰撞路径。然而，在执行MAPF计划时，智能体可能遭遇意外延迟，这可能导致效率低下、死锁甚至碰撞。为解决这些问题，可切换时序规划图提供了一个框架，用于在延迟条件下寻找具有最小执行成本的无环时序规划图，从而确保执行过程无死锁且无碰撞。遗憾的是，现有最优算法（如混合整数线性规划和基于图的可切换边搜索）在实际应用中往往速度过慢。本文提出改进的GSES算法，通过四种加速技术显著提升了GSES的性能：更强的可采纳启发函数、边分组、优先级分支以及增量式实现。在四种不同地图类型及不同智能体数量下进行的实验表明，改进的GSES始终达到GSES两倍以上的成功率，并在两种方法均能成功求解的实例上实现了高达30倍的加速。