In modern fulfillment warehouses, agents traverse the map to complete endless tasks that arrive on the fly, which is formulated as a lifelong Multi-Agent Path Finding (lifelong MAPF) problem. The goal of tackling this challenging problem is to find the path for each agent in a finite runtime while maximizing the throughput. However, existing methods encounter exponential growth of runtime and undesirable phenomena of deadlocks and rerouting as the map size or agent density grows. To address these challenges in lifelong MAPF, we explore the idea of highways mainly studied for one-shot MAPF (i.e., finding paths at once beforehand), which reduces the complexity of the problem by encouraging agents to move in the same direction. We utilize two methods to incorporate the highway idea into the lifelong MAPF framework and discuss the properties that minimize the existing problems of deadlocks and rerouting. The experimental results demonstrate that the runtime is considerably reduced and the decay of throughput is gradually insignificant as the map size enlarges under the settings of the highway. Furthermore, when the density of agents increases, the phenomena of deadlocks and rerouting are significantly reduced by leveraging the highway.

翻译：在现代仓储物流中心中，智能体需要在有限地图上实时完成不断抵达的无限任务，该类问题被形式化为终身多智能体路径规划（lifelong MAPF）。解决这一挑战性问题的目标是：在有限运行时间内为每个智能体找到路径，同时最大化系统吞吐量。然而，现有方法随着地图规模或智能体密度的增长，会出现运行时间指数级增长、死锁与路径重规划等不良现象。为应对终身MAPF中的这些挑战，本文探索了主要应用于一次性MAPF（即预先一次性完成路径规划）的“高速公路”思想，通过引导智能体沿相同方向行驶来降低问题复杂度。我们采用两种方法将高速公路思想融入终身MAPF框架，并讨论了能最大限度减少死锁与路径重规划问题的特性。实验结果表明，在高速公路设置下，随着地图规模增大，运行时间显著减少，吞吐量衰减逐渐趋缓。此外，当智能体密度增加时，利用高速公路能够显著减少死锁与路径重规划现象。