Patrolling Strategy

Patrolling strategy research focuses on optimizing the movement of single or multiple agents (robots, vehicles, or even virtual entities) to effectively monitor and cover a given area, aiming for efficient resource utilization and robust performance. Current research emphasizes developing adaptive algorithms, often employing reinforcement learning or heuristic approaches, to handle dynamic environments, agent failures, and communication constraints, including exploring decentralized and cooperative strategies for multi-agent systems. These advancements have significant implications for various applications, such as security surveillance, environmental monitoring, and disaster response, by improving efficiency, resilience, and fairness in automated patrolling systems.