Abstract: With the emergence of a large number of frequency-using equipment,the radio environment for UAVs to perform tasks has become more and more complex,which puts forward higher requirements for UAVs to recognize the situation and autonomous obstacle avoidance.In view of this,this paper proposes a 3D trajectory planning method for UAVs based on side-end colla-boration.First,a UAV trajectory planning framework with side-end collaboration is proposed,which can synergistically improve the environment perception and autonomous obstacle avoidance capabilities of UAVs under communication connectivity constraints.Second,it proposes an artificial potential field method based on the deep deterministic policy gradient(DDPG) algorithm to avoid UAVs from falling into local minimum points and optimize UAV flight energy consumption.Finally,by performing simulation experiments in static and dynamic interference environments,compared with other trajectory planning methods,the proposed method can optimize the UAV flight trajectory and transmission data rate,which reduces the flight energy consumption of UAVs 5.59% and 11.99% respectively,and improve the transmission data rate 7.64% and 16.52% in static and dynamic interference environments.The proposed method also significantly improves the communication stability and the adaptability of UAVs to complex electromagnetic environments.

Key words: Radio environment map, Mobile edge computing, Trajectory planning, Artificial potential field, Deep reinforcement learning