CTS-PLL: 協調タスク順序付けとマルチエージェント経路探索のための堅牢かつ任意時点で動作するフレームワーク
CTS-PLL: A Robust and Anytime Framework for Collaborative Task Sequencing and Multi-Agent Path Finding
協調タスク順序付けとマルチエージェント経路探索問題に対して、ロックエージェント検出・解放機構と大規模近傍探索による任意時点改良を備えた階層的フレームワークを提案し、高成功率と高品質な解を実現した。
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著者: Junkai Jiang, Yitao Xu, Ruochen Li, Shaobing Xu, Jianqiang Wang
分類: cs.RO
原文アブストラクト
The Collaborative Task Sequencing and Multi-Agent Path Finding (CTS-MAPF) problem requires agents to accomplish sequences of tasks while avoiding collisions, posing significant challenges due to its combinatorial complexity. This work introduces CTS-PLL, a hierarchical framework that extends the configuration-based CTS-MAPF planning paradigm with two key enhancements: a lock agents detection and release mechanism leveraging a complete planning method for local re-planning, and an anytime refinement procedure based on Large Neighborhood Search (LNS). These additions ensure robustness in dense environments and enable continuous improvement of solution quality. Extensive evaluations across sparse and dense benchmarks demonstrate that CTS-PLL achieves higher success rates and solution quality compared with existing methods, while maintaining competitive runtime efficiency. Real-world robot experiments further demonstrate the feasibility of the approach in practice.
