The Study of Highway for Lifelong Multi-Agent Path Finding

TL;DR

This paper introduces a highway-based approach to lifelong multi-agent path finding in warehouses, significantly reducing runtime and deadlocks as map size and agent density increase.

Contribution

It adapts the highway concept from one-shot MAPF to lifelong MAPF, effectively mitigating deadlocks and rerouting issues in dynamic, large-scale environments.

Findings

Runtime is significantly reduced with highway usage.

Deadlocks and rerouting decrease as agent density increases.

Throughput decay becomes negligible on larger maps.

Abstract

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.