提出一种可随时优化的多智能体任务排序与路径规划框架，有效避免碰撞并提升成功率。
CTS-PLL: A Robust and Anytime Framework for Collaborative Task Sequencing and Multi-Agent Path Finding
- 分层架构结合局部重规划与大邻域搜索，实现动态调整
- 密集场景下成功率显著提升，解质量持续优化
- 适用于真实机器人系统，支持实时运行与渐进改进
协同任务排序与多智能体路径规划（CTS-MAPF）问题要求智能体在完成一系列任务的同时避免碰撞，因其组合复杂性带来重大挑战。本文提出CTS-PLL，一种扩展配置式CTS-MAPF规划范式的分层框架，包含两项关键改进：利用完整规划方法实现局部重规划的智能体锁检测与释放机制，以及基于大邻域搜索（LNS）的任何时候（anytime）优化过程。这些改进使算法在高密度环境具有更强鲁棒性，并支持解质量的持续提升。在稀疏与密集基准测试中的大量实验表明，相比现有方法，CTS-PLL在成功率和解质量上表现更优，同时保持了良好的运行效率。真实机器人实验进一步验证了该方法在实际应用中的可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。