Abstract: With the advantages of high flexibility and lightweight, unmanned aerial vehicles (UAVs) have been widely used in data collection of wireless sensor networks. For a multi-UAV-assisted wireless sensor network with randomly distributed and moved users, how to plan the flight paths of the UAVs to effectively collect data from the users remains a challenging problem. This paper aims to maximize the average throughput of data collection in a dynamic environment where the user's location cannot be predicted by optimizing the flight path of the UAVs, which is subject to the shortest flight time and range constraints of UAVs, the constraints of UAV start and end points, the communication distance constraints, the user communication constraints, and the UAV collision avoidance constraints. The resultant problem can be solved by using existing optimization methods with high complexity, which however is difficult to obtain the globally optimal solution. To address this problem efficiently, this paper proposes a deep reinforcement learning algorithm based on Dueling Double DQN (Dueling-DDQN). The proposed algorithm adopts the Dueling network architecture, which enhances the learning ability of the algorithm and improves the robustness and convergence speed of tracked in suboptimal solutions due to the over-estimation on the $ Q $ value. Simulation results show that the proposed algorithm can efficiently obtain the flight paths of multiple UAVs under all constraints. In particular, our proposed algorithm has encouraging convergence and stability performance in comparison with the existing benchmark algorithms.

Key words: UAV communication, data collection, path planning, deep reinforcement learning