Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism

TL;DR

This paper introduces L-MAPF-CM, a novel approach integrating cache mechanisms into lifelong multi-agent path finding to improve efficiency and reduce costs in warehouse scenarios, with dynamic task assignment and cache management.

Contribution

The paper presents a new cache-based mechanism for lifelong MAPF, including a cache grid, task assigner with locking, and evaluation of cache policies, advancing multi-robot planning efficiency.

Findings

Performance improves with high cache hit rates.

Effective under smooth traffic conditions.

Dynamic task assignment enhances coordination.

Abstract

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where agents are continuously reassigned new targets upon completing their current tasks, offers a more realistic approximation of real-world warehouse scenarios. While cache storage systems can enhance efficiency and reduce operational costs, existing approaches primarily rely on expectations and mathematical models, often without adequately addressing the challenges of multi-robot planning and execution. In this paper, we introduce a novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which integrates high-level cache storage with low-level path planning. We have involved a new type of map grid called cache for temporary item storage. Additionally, we involved a task assigner (TA) with a locking mechanism to bridge the gap between the new cache grid and L-MAPF algorithm. The TA dynamically allocates target locations to agents based on their status in various scenarios. We evaluated L-MAPF-CM using different cache replacement policies and task distributions. L-MAPF-CM has demonstrated performance improvements particularly with high cache hit rates and smooth traffic conditions.