Energy Consumption and Communication Quality Tradeoff for Logistics UAVs: A Hybrid Deep Reinforcement Learning Approach

In this paper, we consider a multi-user oriented UAV cargo delivery system, cellular-connected UAV fly to all users within the distribution range successively from the starting point to deliver goods. The UAV needs to complete its mission quickly and maintain good communication with ground base stations (GBSs). To satisfied the above requirements, we propose a three-step approach. Firstly, the influence of cargo weight on UAV energy consumption is considered, we propose a weight change travel salesman problem (WCTSP) to desgin initial trajectory. Secondly, the entire flight trajectory is divided into a series of sub-trajectories base on the obtained initial trajectory. Finally, deep reinforcement learning (DRL) is adopted to optimize all the subtrajectives. By setting reasonable neural network parameters and reward function, the optimal trajectory under the current standard can be obtained after the neural network is trained continuously until it converges. This paper aims to minimize the weighted sum of total energy consumption and total outage time by jointly optimizing cargo distribution scheduling, communication scheduling and UAV flight strategy. The simulation results demonstrate the effectiveness of our proposed trajectory optimization scheme.