CTS-CBS：マルチエージェント協調タスク順序付けと経路探索の新手法
CTS-CBS: A New Approach for Multi-Agent Collaborative Task Sequencing and Path Finding
エージェントが指定順にタスク地点を訪問しつつ衝突回避経路を計画するCTS-MAPF問題に対し、二段階探索で完全性と最適性を保証するCTS-CBSを提案し、実験で成功率と速度の大幅な向上を示した。
著者: Junkai Jiang, Ruochen Li, Yibin Yang, Yihe Chen, Yuning Wang, Shaobing Xu, Jianqiang Wang
分類: cs.RO, cs.MA
原文アブストラクト
This paper addresses a generalization problem of Multi-Agent Pathfinding (MAPF), called Collaborative Task Sequencing - Multi-Agent Pathfinding (CTS-MAPF), where agents must plan collision-free paths and visit a series of intermediate task locations in a specific order before reaching their final destinations. To address this problem, we propose a new approach, Collaborative Task Sequencing - Conflict-Based Search (CTS-CBS), which conducts a two-level search. In the high level, it generates a search forest, where each tree corresponds to a joint task sequence derived from the jTSP solution. In the low level, CTS-CBS performs constrained single-agent path planning to generate paths for each agent while adhering to high-level constraints. We also provide heoretical guarantees of its completeness and optimality (or sub-optimality with a bounded parameter). To evaluate the performance of CTS-CBS, we create two datasets, CTS-MAPF and MG-MAPF, and conduct comprehensive experiments. The results show that CTS-CBS adaptations for MG-MAPF outperform baseline algorithms in terms of success rate (up to 20 times larger) and runtime (up to 100 times faster), with less than a 10% sacrifice in solution quality. Furthermore, CTS-CBS offers flexibility by allowing users to adjust the sub-optimality bound omega to balance between solution quality and efficiency. Finally, practical robot tests demonstrate the algorithm's applicability in real-world scenarios.
