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.

In this paper, the intelligent cooperative control problem of unmanned surface vehicles (USVs) is investigated. We consider a team of USVs used in the protection of a target against potential threats. The task of the USVs is to cooperatively protect the target from being destroyed. A behavior-based fuzzy logic control system is presented to solve the problem. First, we identify two types of behaviors; low and high level behaviors. High level behaviors act as a mechanism to induced coordination and synchronization among USVs. Based on this, four high level behaviors (behavior integrator, intercept, formation and assist behaviors), each implementing a fuzzy inference system and two low level behaviors(collision avoidance and attack behaviors) are proposed. The paper defines behavior-based rules to meet the requirement of a simple operational scenario. Genetic algorithms is used to learn the rule base and the choice of behavior. Simulation results demonstrates the effectiveness of our approach. Results shows that the approach has potential application for a real team of cooperative USVs.

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of Learning Classifier System and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier system and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.