多机器人巡逻算法自动分区，提升巡检效率与指挥站态势感知。
Distributed Algorithm with Emergent Area Partitioning and Base Station's Situation Awareness for Multi-Robot Patrolling
- 机器人基于局部信息自主决策，统一评估巡检需求与上报紧迫性。
- 仿真中全覆盖所有目标区域，比现有方法更优，抗通信中断和故障。
- 无需全局规划，自动形成区域划分，适合复杂环境中的协同巡逻。
多机器人巡逻可高效侦测并应对异常情况，但需提升巡检效率与指挥站的态势感知能力。本研究提出一种新型多机器人巡逻算法——局部反应式分区（LR-PT）。该算法使机器人基于本地信息自主选择巡逻目标，将巡检需求与向基站汇报进度的紧迫性整合进统一效用函数。实验表明，该方法在仿真中实现了对所有感兴趣区域的高频覆盖，显著提升基站点的态势感知能力。算法具备良好的鲁棒性，在通信受限或机器人故障情况下仍能稳定运行。同时，系统能自主生成区域划分，避免陷入局部最优，实现全任务区域的全面覆盖。结果验证了LR-PT在群体智能优势基础上，有效应对真实场景中的操作挑战。
原文摘要 · Abstract (English)
Patrolling with multiple robots offers efficient surveillance to detect and manage undesired situations. This necessitates improved patrol efficiency and operator situation awareness at base stations. Enhanced situation awareness enables operators to predict robots' behaviors, support recognition and decision-making, and execute emergency interventions. This study presents the Local Reactive and Partition (LR-PT) algorithm, a novel multi-robot patrolling approach. In simulations, LR-PT outperformed existing methods by ensuring frequent patrols of all locations of interest and enhancing the situation awareness of the base station. Robots independently select patrol targets based on locally available information, integrating patrol needs and the urgency of reporting mission progress to the base station into a unified utility function. This locality also contributes to robustness against communication constraints and robot failures, as demonstrated in this research. The algorithm further autonomously emerged the area partition, which can avoid falling into local optima and realize the comprehensive patrol over the whole mission area. The simulation results demonstrated the superior performance of LR-PT for multi-robot patrolling, utilizing the advantages of swarm robotics and addressing real-world operational challenges.
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