Abstract
The multi-UAV system has stronger robustness and better stability in combat. Therefore, the collaborative penetration of UAVs has been extensively studied in recent years. Compared with general static combat scenes, the dynamic tracking and interception of equipment penetration are more difficult to achieve. To realize the coordinated penetration of the dynamic-tracking interceptor by the multi-UAV system, the intelligent UAV model is established by using the deep deterministic policy-gradient algorithm, and the reward function is constructed using the cooperative parameters of multiple UAVs to guide the UAV to proceed with collaborative penetration. The simulation experiment proved that the UAV finally evaded the dynamic-tracking interceptor, and multiple UAVs reached the target at the same time, realizing the time coordination of the multi-UAV system.
Highlights
Compared with traditional manned aerial vehicles, unmanned aerial vehicles (UAVs) can be autonomously controlled or remotely controlled, which have the advantages of low requirements on the combat environment and strong battlefield survivability, and they can be used to perform a variety of complex tasks [1,2]
Aimed at the environment with dynamic threats, this paper proposes a method based on deep reinforcement learning to achieve multi-UAV-cooperative penetration
Algorithm, we establish the intelligent UAV model and realize the multi-UAV-cooperative penetration dynamic-tracking interceptor by designing the reward function related to coordination and penetration
Summary
Compared with traditional manned aerial vehicles, unmanned aerial vehicles (UAVs) can be autonomously controlled or remotely controlled, which have the advantages of low requirements on the combat environment and strong battlefield survivability, and they can be used to perform a variety of complex tasks [1,2]. Aimed at the environment with dynamic threats, this paper proposes a method based on deep reinforcement learning to achieve multi-UAV-cooperative penetration. Through the investigation of relevant literature, it can be found that the application of deep reinforcement learning to multi-UAV systems is a feasible method, which can be used to achieve complex multi-UAV-system tasks and has great research potential. Based on the deep reinforcement-learning DDPG algorithm, we establish the intelligent UAV model and realize the multi-UAV-cooperative penetration dynamic-tracking interceptor by designing the reward function related to coordination and penetration. The simulation experiment results show that the trained multi-UAV system can achieve cooperative attack tasks from different initial locations, which proves the application potential of artificial intelligence methods, such as reinforcement learning in the implementation of coordinated tasks in UAV clusters
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