Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader–follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader’s trajectory. This paper studies the leader–follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.
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