Effective search strategy for moving targets in unknown environments using multiple robots

Robots are widely used for target search in applications such as search and rescue, environmental monitoring, and surveillance. Existing search algorithms typically rely on target signals or predictable movement patterns, which might not be available in real applications. In this paper, we address the practical challenge of a target search problem where targets do not emit signals and have unpredictable movement patterns. The problem is formulated as an area coverage problem: how to maximize the coverage of the robots' detection areas within a limited time. We propose an algorithm that divides the search area into multiple partitions and assigns specific partitions to robots for maximizing their coverage and the success rate of target detection. Within each partition, the random walk technique is adopted by the robots to handle robot failures and unknown obstacles. Through theoretical analysis and experiments, we explore the optimal number of partitions as well as the optimal partition shape to facilitate searching. Extensive simulations across different dynamic environments validate the effectiveness and adaptability of our proposed algorithm. Copyright © 2024 IEEE.