Multi Agent Path Finding (MAPF) is critical for coordinating multiple robots in shared environments, yet robust execution of generated plans remains challenging due to operational uncertainties. The Action Dependency Graph (ADG) framework offers a way to ensure correct action execution by establishing precedence-based dependencies between wait and move actions retrieved from a MAPF planning result. The original construction algorithm is not only inefficient, with a quadratic worst-case time complexity it also results in a network with many redundant dependencies between actions. This paper introduces two key improvements to the ADG framework. First, we prove that wait actions are generally redundant and show that removing them can lead to faster overall plan execution on real robot systems. Second, we propose an optimized ADG construction algorithm, termed Sparse Candidate Partitioning (SCP), which skips unnecessary dependencies and lowers the time complexity to quasi-linear, thereby significantly improving construction speed.

翻译：多智能体路径规划（MAPF）对于在共享环境中协调多个机器人至关重要，然而由于操作不确定性，生成路径规划的鲁棒执行仍具挑战性。动作依赖图（ADG）框架通过建立从MAPF规划结果中提取的等待动作与移动动作间基于优先级的依赖关系，为保障动作正确执行提供了一种方法。原始构建算法不仅效率低下（其最坏情况时间复杂度为二次方），还会产生包含大量动作间冗余依赖的网络。本文提出了对ADG框架的两项关键改进。首先，我们证明等待动作通常是冗余的，并表明在实际机器人系统中移除这些动作可以加速整体规划的执行。其次，我们提出一种优化的ADG构建算法，称为稀疏候选划分（SCP），该算法跳过不必要的依赖关系并将时间复杂度降低至拟线性，从而显著提升了构建速度。