The instability of the underwater environment and underwater communication brings great challenges to the coordination and cooperation of the multi-Autonomous Underwater Vehicle (AUV). In this paper, a multi-AUV dynamic maneuver countermeasure algorithm is proposed based on the interval information game theory and fractional-order Differential Evolution (DE), in order to highlight the features of the underwater countermeasure. Firstly, an advantage function comprising the situation and energy efficiency advantages is proposed on account of the multi-AUV maneuver strategies. Then, the payoff matrix with interval information is established and the payment interval ranking is achieved based on relative entropy. Subsequently, the maneuver countermeasure model is presented along with the Nash equilibrium condition satisfying the interval information game. The fractional-order DE algorithm is applied for solving the established problem to determine the optimal strategy. Finally, the superiority of the proposed multi-AUV maneuver countermeasure algorithm is verified through an example.

Rapidly developing deep reinforcement learning (DRL) methods are suited to address the increasingly complex task requirements of unmanned surface vehicles (USV) that traditional methods cannot resolve. However, current DRL methods fail to make full use of the USV dynamics characteristics and do not consider unknown external disturbances. This article presents a state prediction reinforcement learning (SPRL) framework, to enhance the robustness against disturbances in obstacle avoidance navigation tasks for USVs. A novel latent robust world model (LRWM) with double latent features is elaborated based on probabilistic graphical model (PGM) to learn the transition of the USV navigation and predict future latent features against disturbances. A motion feature is learned to capture the impact of environmental disturbances on USV dynamics, while a prediction feature is extracted to predict future motion states. The proposed SPRL method can provide both the actor and the critic with essential prior information, thereby facilitating its application to actor–critic-based DRL methods. We performed field experiments in a lagoon environment, demonstrating the proposed method's deployability on practical USV platforms and its effectiveness in obstacle avoidance navigation under unknown disturbances.

This paper presents a formation control strategy for unmanned surface vehicles (USVs) with sensing constraints moving in a leader‐follower formation. Each USV is assumed to be equipped with a vision‐based sensor, which is able to get the line‐of‐sight (LOS) range and bearing information. Most existing literature assumes that the USVs in formation control are with no sensing limitations or with 360‐degree sensing fields; however, in our research, the vision‐based sensor’s capability is restricted due to limited Field of View (FOV) and visual range. We consider that each USV in formation problem is equipped with a sector‐like sensing field sensor for the leader‐follower formation in two‐dimensional space. The formation controller is developed by employing backstepping control technique and exponential remapping. The backstepping controller is designed to stabilize the triangular formation of three USVs, and the proposed exponential remapping method is to deal with the sector‐like sensing constraint problem. Comparative analysis with three exponential remapping methods using numerical simulations is given to demonstrate the effectiveness of the proposed method.

This study presents a new adaptive trajectory tracking control scheme for a fully actuated Unmanned Surface Vehicle (USV) to track a common moving target region. In this control concept, the desired objective trajectory is specified as a moving region instead of a moving point, and so which is called non-strict trajectory tracking. Within this control scheme, a regression matrix is used to handle the parameter uncertainties, and region-based control scheme is used to track a desired moving region. A switching gain control term based on the exponential function is proposed to make the USV’s trajectory converge into the desired moving region rather than converge on the boundary of the moving region, and to reduce system chattering at the same time. A Lyapunov-like function is presented for stability analysis of the proposed control scheme. Numerical simulations are conducted to demonstrate the performance of the proposed non-strict trajectory tracking control scheme of the USV.