The Study of Highway for Lifelong Multi-Agent Path Finding
This addresses efficiency and scalability issues in warehouse automation for logistics, but it is incremental as it adapts an existing highway concept from one-shot MAPF to the lifelong setting.
The paper tackles the lifelong Multi-Agent Path Finding problem in warehouses by introducing highways to reduce runtime and minimize deadlocks and rerouting, showing that runtime is considerably reduced and throughput decay becomes insignificant as map size grows.
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.