Trajectory Optimization for Connectivity-Aware Inspection UAV: A Hybrid Algorithm of DRL and SA

In this paper, we propose an inspection system based on a cellular-connected unmanned aerial vehicle (UAV), where UAV departs from the starting point and flies to the inspection points for patrol inspection. The purpose of our work is to minimize the total inspection service time of the UAV, while ensuring that the total communication outage time throughout the flight is less than a certain threshold. The total inspection service time encompasses the cumulative time spent by the UAV traveling to each inspection point. To address the non-convex problem, we propose a hybrid algorithm that combines deep reinforcement learning (DRL) with simulated annealing (SA). Initially, we employ DRL to determine the trajectory between any two points within the defined scenario, followed by the utilization of SA to derive the optimal inspection sequence. The numerical results show that the total inspection service time obtained by our proposed algorithm is always lower than the benchmark algorithm, and the effect is better when the threshold is smaller, about 10% lower than the benchmark algorithm.