Intelligent resource scheduling scheme for UAV swarm collaborative sensing

With the rapid development of the low-altitude economy, unmanned aerial vehicles (UAV) have been widely applied in monitoring and sensing tasks. However, the limited onboard computing resources of UAV constrain the efficient processing of sensing data. Moreover, overlapping observation areas in collaborative sensing introduce additional computational redundancy. Meanwhile, the highly dynamic network topology and fluctuating node resources significantly increase the complexity of resource coordination. To address these challenges, an intelligent resource scheduling scheme for UAV swarm collaborative sensing was proposed. Adaptive sensing mode selection, stepwise computation offloading, and competitive bandwidth allocation were integrated to achieve heterogeneous resource coordination across communication, sensing, and computation (CSC), thereby enhancing collaborative sensing efficiency. Furthermore, a multi-agent reinforcement learning (MARL) algorithm with an attention mechanism was employed to solve the optimization problem, enabling agents to extract critical environmental features more effectively. Simulation results demonstrate that, compared with benchmark schemes, the proposed scheme significantly reduces the execution time of sensing tasks while improving computational resource utilization.