In Internet of Underwater Things (IoUT) environments, cooperative pursuit of highly maneuverable unidentified underwater vehicles (U-UUVs) by multiple autonomous underwater vehicles (AUVs) remains a fundamental challenge due to uncertainty and adversarial behaviors. To address this problem, we propose a cooperative pursuit framework with centralized training and decentralized execution for IoUT systems. Within this framework, a Brain-Inspired Hierarchical Memory Distributional Soft Actor–Critic (BAC) algorithm is developed. Multiple AUVs are modeled as intelligent IoUT nodes that are trained via reinforcement learning to learn policies from local observations and achieve policy coordination. To cope with environmental non-stationarity, a hierarchical memory mechanism and safety-constrained policy optimization are incorporated to improve learning stability, sample efficiency, and operational safety under complex seabed terrain and time-varying ocean currents. Theoretical analysis establishes conditional results on policy improvement and optimality for the corresponding constrained formulation under the stated assumptions. Hardware-in-the-loop simulations with realistic bathymetry and ocean currents demonstrate that the proposed method significantly outperforms baseline approaches, reducing pursuit time.

Powered by wind, unmanned sailboats are highly suitable for performing coverage patrol missions around remote islands with limited supply and energy support. However, due to the complexity of island environments and the underactuated motion characteristics of unmanned sailboats, their coverage path planning (CPP) becomes a significant challenge. This paper proposes a multi-strategy CPP framework that overcomes the limitations of existing unmanned sailboat coverage methods. First, a Downwind Priority Greedy Dynamic Reward Algorithm is used to assign reward values to grid nodes based on wind direction and the sailboat’s traveled route, guiding the sailboat to prioritize exploration of downwind grid nodes and ultimately complete the coverage task. Second, during tacking maneuvers, an improved Artificial Potential Field method is applied to adjust the path and prevent the sailboat from entering no-sail zones. Furthermore, when the sailboat becomes stuck in a deadlock, a Velocity Prediction Program Rapidly-exploring Random Tree Star Algorithm is employed to resolve it. Finally, Simulation results show that the proposed framework exhibits strong adaptability across different map environments. Compared with existing sailboat CPP algorithms, the proposed method demonstrates clear advantages in both feasibility and performance.

The polar iceberg observation using autonomous underwater vehicles (AUVs) is of great scientific value in revealing global warming mechanisms and promoting Internet of Underwater Things (IoUT) development. In order to cope with the challenge of complex boundaries and unknown environments in iceberg observation mission, this paper proposed an Environ-ment information-driven online Dual-layer Coverage Path Planning Method (EDCPPM) for polar iceberg observation in unknown environments. Firstly, Boundary Adaptive Coverage Algorithm based on Grid Completeness (BACGC) is proposed, which is able to adaptively insert boundary path points to opti-mize boundary coverage paths to improve coverage completeness. Secondly, an Environmental Information-driven Dynamic Neural Network (EIDNN) algorithm is designed to achieve online replan-ning of iceberg interest regions and to enhance escaping efficiency from dead zones in order to reach demand of observation accuracy. It is demonstrated that EDCPPM method proposed shows strong applicability and advancement, and can effectively improve the performance of iceberg observation mission.

Autonomous Underwater Vehicle (AUV) is crucial to the Internet of Underwater Things setup and upkeep. However, Flow Fields (FFs) involving internal waves and submerged currents exhibit spatiotemporal diversity. Differences in their characteristics lead to an adaptability bottleneck for Path Planning (PP). Motivated by this challenge, an adaptability-oriented PP method, which is termed Continual Distributional Soft Actor-Critic (CDSAC), is proposed. Note that most existing learning-based methods perform well in a specific FF. When AUV operates across spatiotemporal shifts, however, FF variation necessitates extensive retraining of these methods for adaptation. CDSAC incorporates an Anti-Forgetting Replay (AFR) and a Policy Learning Accelerator (PLA) to improve the adaptability and generalization to different FFs. Here, AFR enhances the resistance to catastrophic forgetting (CF) for historical FFs. After that, PLA extracts the invariant features from different FFs and filters out spurious features to achieve a rapid convergence. Extensive simulations using actual FF data show that the proposed method has excellent adaptability in various FFs. Testing on a Hardware-In-the-Loop simulation platform shows the resistance to CF and generalization to unseen FFs of the trained model.

A deep understanding of the particularity of under-water emergency search and rescue missions is of great significa-nce for the development of Internet of Underwater Things (IoUT). In various domains related to IoUT, utilizing Autonomous Unde-rwater Vehicle (AUV) for autonomous search and rescue (SAR) missions has become an inevitable trend. Considering the parti-cularrity of underwater emergency SAR mission, we proposed a hybrid-algorithm-based emergency search and rescue (HASAR) method for target search in uncertain environment. First, the improved IBoustrophedon-BINN algorithm is used to calculate global path in uncertain environment to obtain the target posi-tion. In addition, to ensure timeliness of SAR missions, a IACO algorithm is designed to replan the optimal energy consumption path to obtain identity of target. During the AUV tracking repl-anning path process, complex obstacles which include static and dynamic may be encountered. Therefore, the ISSA-IDWA algo-rithm with adaptive coefficient is utilized to obtain real-time path to avoid obstacles. Extensive simulation results and practical ex-periments verify the performance of proposed HASAR method.

Addressing challenges like ocean currents, dynamic obstacles, collision avoidance, and computing limitations, a hybrid algorithm is proposed for multi-AUVs to search for random static or dynamic targets in a 3D marine environment and the environment is characterized by our Task Urgency Biological Neural Network (TUBNN) model, encapsulates factors such as currents, obstacles, and targets using neuronal activity representations. For optimizing task assignments in the face of unpredictable oceanic variables, a Double Layer Biological Self-organizing Map (DLBSOM) algorithm is proposed. Post assignment, the initial path, generated via DLBSOM, is refined by the end-to-end direct connection model to eliminate superfluous routes. Enhanced by the A$^\ast$algorithm and Fermat’s spiral curve, our approach ensures paths align with AUV navigational constraints. Integrating real-time sonar data, our Distance-Speed Comprehensive Evaluation (DSCE) strategy, combined with the local dynamic neural network, facilitates the avoidance of unforeseen barriers, ensuring safe AUV navigation. The correction redistribution mechanism, leveraging live data and assessing AUV and target statuses, refines task assignment. We holistically evaluate environmental, cooperative, and AUV-specific constraints, with our algorithm’s efficacy validated through rigorous theoretical analysis, simulations, and field tests.