Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works either do not consider agent attrition or impose significant communication requirements to enable adaptation. In this work, we present the Adaptive Heuristic-based Patrolling Algorithm, which is capable of adaptation to agent loss using minimal communication by taking advantage of Voronoi partitioning. Additionally, we provide new centralized and distributed mathematical programming formulations of the patrolling problem, analyze the properties of Voronoi partitioning, and show the value of our adaptive heuristic algorithm by comparison with various benchmark algorithms using a realistic simulation environment based on the Robot Operating System (ROS) 2.

翻译：多智能体巡逻是入侵检测、区域监控及治安巡逻等多个领域中的关键问题，涉及由一组智能体对环境中的指定点进行重复访问。尽管该问题已有广泛研究，但现有工作大多未考虑智能体损耗问题，或需依赖显著通信开销来实现自适应。本文提出了基于自适应启发式的巡逻算法，该算法通过利用沃罗诺伊分区，能够在最小通信开销下适应智能体损失。此外，我们提供了新的集中式与分布式巡逻问题数学规划形式，分析了沃罗诺伊分区的性质，并通过基于机器人操作系统（ROS）2的逼真仿真环境与多种基准算法进行对比，展示了所提自适应启发式算法的价值。