让多机器人系统高效协同执行重复任务，实时调整计划并同步动作。
Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic
- 先离线生成计划，再在线动态协调，通过实时通信调整策略。
- 9台机器人实测显示适应性优于旧方法，90个智能体仿真验证可扩展性。
- 适合需要长期重复任务的机器人协作场景，如仓储、巡检。
本文研究在重复任务下基于线性时序逻辑（LTL）规范的多机器人系统规划问题。为高效求解，提出一种自下而上的方法，结合离线计划生成与在线协调机制，通过实时通信动态调整计划。针对动作延迟问题，引入同步机制以确保任务执行的协调性，构建了一个可广泛适用于多机器人应用的多智能体协同与同步框架。软件基于Python和ROS2开发，便于部署。实验中使用9台机器人验证了方法的适应性优势；同时通过最多90个代理的仿真，展示了算法计算复杂度降低及良好的可扩展性。
原文摘要 · Abstract (English)
We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.
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