As an emerging direction of multi-agent collaborative control technology, multiple autonomous underwater vehicle (multi-AUV) cooperative area search technology has played an important role in civilian fields such as marine resource exploration and development, marine rescue, and marine scientific expeditions, as well as in military fields such as mine countermeasures and military underwater reconnaissance. At present, as we continue to explore the ocean, the environment in which AUVs perform search tasks is mostly unknown, with many uncertainties such as obstacles, which places high demands on the autonomous decision-making capabilities of AUVs. Moreover, considering the limited detection capability of a single AUV in underwater environments, while the area searched by the AUV is constantly expanding, a single AUV cannot obtain global state information in real time and can only make behavioral decisions based on local observation information, which adversely affects the coordination between AUVs and the search efficiency of multi-AUV systems. Therefore, in order to face increasingly challenging search tasks, we adopt multi-agent reinforcement learning (MARL) to study the problem of multi-AUV cooperative area search from the perspective of improving autonomous decision-making capabilities and collaboration between AUVs. First, we modeled the search task as a decentralized partial observation Markov decision process (Dec-POMDP) and established a search information map. Each AUV updates the information map based on sonar detection information and information fusion between AUVs, and makes real-time decisions based on this to better address the problem of insufficient observation information caused by the weak perception ability of AUVs in underwater environments. Secondly, we established a multi-AUV cooperative area search system (MACASS), which employs a search strategy based on multi-agent reinforcement learning. The system combines various AUVs into a unified entity using a distributed control approach. During the execution of search tasks, each AUV can make action decisions based on sonar detection information and information exchange among AUVs in the system, utilizing the MARL-based search strategy. As a result, AUVs possess enhanced autonomy in decision-making, enabling them to better handle challenges such as limited detection capabilities and insufficient observational information.

This article presents a nonlinear adaptive line-of-sight path following controller for underactuated autonomous underwater vehicles in the presence of ocean currents. Firstly, a new nonsingular path following error kinematic model in the Serret–Frenet frame is developed, where a nominal course angle error is introduced to significantly simplify the guidance law design. Secondly, an adaptive line-of-sight guidance law with the introduction of the current observer is proposed to make the vehicle produce a variable sideslip angle to compensate for the drift force for any parametric curved-path path following. Benefit from the global κ -exponential convergence property of the designed current observer, the actual course angle error can be eliminated indirectly. Then, dynamic controller built on Lyapunov theory and backstepping technique guarantee the uniform global exponential stability of the yaw and relative surge velocity. In the end, stability analysis shows that the global κ -exponential stability is achieved for the closed-loop system. Simulation results demonstrate the effectiveness of the proposed control scheme.

This article addresses one quadruple-rudder allocation method for an autonomous underwater vehicle (AUV) equipped with X rudder, in which all of the rudders can be operated independently. By considering X rudder’s character, one X-rudder AUV’s control information frame is designed. It contains the anti-normalization method based on virtual rudders and one quadruple-rudder allocation with Lévy flight character. This quadruple-rudder allocation method has the advantages of Lévy flight and avoids the shaking problem. One contrast simulation of a lawn mower path following mission in three-dimensional (3D) space is performed. The results of simulation show that the quadruple-rudder allocation with Lévy flight character can offer accurate and reliable control ability. Besides that, compared to the quadruple-rudder allocation based on pseudoinverse and fixed point iteration, the method designed with Lévy flight character can achieve the same mission with less X rudder’s operation.

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.