キャッシュ機構による生涯マルチエージェント経路探索の強化
Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism
倉庫運用向けの生涯マルチエージェント経路探索に、一時的な物品保管用のキャッシュグリッドとロック機構付きタスク割り当てを組み込み、経路計画と連携させる手法を提案した。
著者: Yimin Tang, Zhenghong Yu, Yi Zheng, T. K. Satish Kumar, Jiaoyang Li, Sven Koenig
分類: cs.RO, cs.AI
原文アブストラクト
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.
