Defense systems are usually deployed to protect high-value targets or hot spots that are integral parts of the modern battlefield environment. However, in coastal defense operations (due to the variability of the maritime environment and the sustainability of combat), limited operational capabilities, the need for efficient coordination, and protracted combat are peculiarly challenging to meet by traditional manned fleets. In contrast, with lower costs, unmanned fleets can organize an autonomous defense against enemy targets that are capable of rapid response. This paper focuses on the typical defense scenario; we analyzed and modeled the objective functions of the intelligent defense system and propose a hierarchical distributed multi-agent-based system design scheme. Finally, to test the system’s performance, we established simulation verification experiments in a typical scenario and compared the system based on the traditional central architecture. The results show that, in a defense operation, the hierarchically-distributed multi-agent-based system shows improvements in system decision-making efficiency and interception effect.
Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing autonomous unmanned systems is the ability to perform complex group missions, which require coordination and collaboration among multiple platforms. This paper presents a cooperative navigating task approach that enables multiple unmanned surface vehicles (multi-USV) to autonomously capture a maneuvering target while avoiding both static and dynamic obstacles. The approach adopts a hybrid multi-agent deep reinforcement learning framework that leverages heuristic mechanisms to guide the group mission learning of the vehicles. Specifically, the proposed framework consists of two stages. In the first stage, navigation subgoal sets are generated based on expert knowledge, and a goal selection heuristic model based on the immune network model is used to select navigation targets during training. Next, the selected goals’ executions are learned using actor-critic proximal policy optimization. The simulation results with multi-USV target capture show that the proposed approach is capable of abstracting and guiding the unmanned vehicle group coordination learning and achieving a generally optimized mission execution.
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