Spatiotemporal Precise Routing Strategy for Multi-UAV-Based Power Line Inspection Using Hybrid Network of FANET and Satellite Internet

Multiple unmanned aerial vehicles (UAV) based power line inspection is the key technology for diagnosing faults and facilitating rapid power restoration in disaster-affected power systems. However, due to limited UAV battery capacity and disrupted ground communication during disasters, the inspection range and operational duration of UAVs are limited. This paper utilizes a hybrid network composed of UAV flying ad hoc network(FANET) and satellite internet to provide communication support for multi-UAV-based power line inspections under disasters. With the aim of achieving stable communication time delay and balanced energy consumption within the UAV swarm, the multi-agent Q-learning algorithm is employed for training, and the obtained spatiotemporal precise routing strategy enables UAVs to select the optimal routing path at each time point, so as to improve video transmission quality and inspection coverage capability, and realize the real-time precise monitoring and diagnosing of power line damage during disaster. Simulation results show that the proposed method outperforms traditional routing algorithms in terms of end-to-end delay stability and energy consumption balance.