A search and rescue task allocation algorithm for UAV swarms based on dynamic power management

Post-disaster search and rescue missions have extremely high requirements for timeliness and resource utilization efficiency. Owing to their flexibility, increasingly diverse remote sensing payloads, and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have become an important part of post-disaster rescue efforts. However, the capabilities of a single UAV are limited, and a multi-UAV collaborative system is required to improve mission effectiveness, among which the task allocation strategy is the key factor affecting the overall performance. Previous studies have not fully considered the dynamic coupling relationship between UAV payload and battery capacity, nor have they collaboratively optimized material and battery resources during the allocation process, which limits the improvement of the mission completion rate. In this study, we propose a Dynamic Task Allocation algorithm for Search and Rescue (DTASR). First, a system model is established to quantify the dynamic impact of the load on power consumption and flight range. Second, a two-stage allocation mechanism for search and collaborative rescue is designed, and a multi-objective optimization problem is balanced by introducing a multi-value function. A collaborative material allocation strategy based on the remaining battery capacity is proposed to achieve the efficient collaborative utilization of battery power and materials. The simulation results show that DTASR can maintain a stable mission completion rate under different scenario scales, and its performance is significantly better than those of the existing mainstream algorithms. These results verify the effectiveness and robustness of the algorithm in dynamic post-disaster environments.