An Optimal Multi-Agent Path Planning Method Based on Priority Setting and Collision Detection

In traditional multi-agent path planning research, algorithms typically focus on single-instance path planning problems, with limited consideration given to path disruptions caused by unforeseen circumstances. When such disruptions occur, re-invoking global path planning algorithms often neglects the agent's prior state of movement, which can lead to reduced efficiency, such as unnecessary deceleration. This paper proposes a novel path planning algorithm based on Yen's algorithm, aimed at addressing the issue of inconsistencies between the initial paths and subsequently planned paths. During experimental tests, the proposed method demonstrates its ability to effectively enhance both the path planning efficiency and fault tolerance of multi-agent systems, offering a better solution for multi-agent path planning.