多机器人巡逻系统实时应对动态环境变化，提升监控效率。
Practical Handling of Dynamic Environments in Decentralised Multi-Robot Patrol
- 采用完全去中心化的在线机制感知并响应环境动态变化。
- 在高度动态场景中，性能显著优于现有基线方法。
- 适用于安全巡检、灾后救援等需自适应响应的场景。
多机器人团队持续监控在安防、环境监测和灾后恢复等领域具有重要意义。在完全在线的去中心化模式下执行监控，能显著提升系统的鲁棒性、适应性和可扩展性，理论上可实时适应环境变化。本文聚焦多机器人巡逻任务，即机器人团队需持续最小化对关键点的访问间隔，且在路径可通行性高度动态变化的环境中运行。此类动态必须由巡逻代理实时观测，并在完全去中心化的在线方式下进行处理。本文提出一种新的去中心化多机器人巡逻方法，用于监测并调整环境动态。实验表明，该方法在高度动态场景中显著优于现实基线；同时，研究还分析了在某些动态场景中显式建模环境动态可能不必要或不可行的情况。
原文摘要 · Abstract (English)
Persistent monitoring using robot teams is of interest in fields such as security, environmental monitoring, and disaster recovery. Performing such monitoring in a fully on-line decentralised fashion has significant potential advantages for robustness, adaptability, and scalability of monitoring solutions, including, in principle, the capacity to effectively adapt in real-time to a changing environment. We examine this through the lens of multi-robot patrol, in which teams of patrol robots must persistently minimise time between visits to points of interest, within environments where traversability of routes is highly dynamic. These dynamics must be observed by patrol agents and accounted for in a fully decentralised on-line manner. In this work, we present a new method of monitoring and adjusting for environment dynamics in a decentralised multi-robot patrol team. We demonstrate that our method significantly outperforms realistic baselines in highly dynamic scenarios, and also investigate dynamic scenarios in which explicitly accounting for environment dynamics may be unnecessary or impractical.
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