Radio Map-Based Delivery Sequence Design and Trajectory Optimization in UAV Cargo Delivery Systems

In this paper, we consider a UAV-assisted cargo delivery system with limited payload capacity. Due to the limited load capacity of the cargo UAV, it needs to make multiple trips to the warehouse to pick up the parcels. Meanwhile, due to the uneven distribution of cellular signal strength in the air, to send logistics information to ground users (GUs) in time, the cellular-connected UAV needs to bypass the weak signal area in the air. Therefore, these two factors lead to the increase of the total cargo delivery time. To reduce the total delivery time and ensure the communication quality of the UAV, we formulate an objective function to be optimized, which is the weighted sum of the delivery time and the communication outage time of the cargo UAV. We propose a limited payload UAV delivery (LP-UAV-D) framework to solve this problem. The framework consists of the particle swarm optimization (PSO) algorithm and the dueling double deep Q network (D3QN) algorithm. We used two classic algorithms as control groups. The numerical results show that regardless of the maximum payload or flight speed of the UAV, the objective function value obtained through our proposed LP-UAV-D framework and with the help of radio maps is always the smallest. Specifically, the performance of solving the trade-off problem between delivery time and communication quality is improved by about 10%-20% compared with the two comparison algorithms.