Cooperative Target Search Algorithm for UAV Swarms With Limited Communication and Energy Capacity

Target search by unmanned aerial vehicles (UAV) has wide applications in rescue, round-up, and border patrol. However, a single UAV cannot satisfy target search in a wide region with limitations of sensing range, search time capacity, etc. Compared with a single UAV, UAV swarms have higher performance in target search, while communication, energy consumption, and cooperation efficiency have limitations. In this article, we propose a UAV swarms cooperative search model (USCSM) with the limitations of communication and energy capacity. The proposed model is modelled as an exact potential game to complete it efficiently, and we introduce a binary log-linear learning jointing dung beetle optimizer algorithm (BLLL-DBO) to optimize the proposed model. The simulation results indicate that the suggested method outperforms existing algorithms in terms of region coverage rate and target search efficiency.