Search Results (154)

To avoid predators, benthic fish will stir up the sediment on the seabed by flapping their pectoral fins, thus burying themselves. This self-burial concealment strategy can offer bionic enlightenment for the benthic residence method of Unmanned Underwater Vehicles (UUVs). In this paper, based on the observation results of the self-burial behavior of benthic fish, a two-dimensional fluid-particle numerical model was developed to simulate the processes of sediment transport induced by pectoral fin flapping. In addition, an orthogonal experimental design was employed to analyze the effects of body length, flapping amplitude, flapping number, flapping frequency, and particle size on burial ratio, input power, and burial efficiency. The results reveal that rapid pectoral fin flapping enables benthic fish to fluidize sediments and achieve self-burial. Among the influencing factors, body size has the most significant impact on coverage ratio and input power, as larger fish generate stronger tip vortices and fluid disturbances, making local flow velocities more likely to exceed the critical starting velocity. In contrast, particle size has the weakest effect on burial performance, while kinematic parameters exert a far greater impact on self-burial than environmental parameters. The research results can offer references for the biomimetic design of self-burying UUVs. Full article

In many aquaculture farms, Unmanned Underwater Vehicles (UUVs) are being deployed to perform dangerous and time-consuming repetitive tasks (e.g., fish net-pen visual inspection) on behalf of or in collaboration with farm operators. Mostly, they are remotely operated, and one of the main barriers to deploying them autonomously is the UUV localisation. Specifically, the cost of the localisation sensor suite, sensor reliability in constrained operational workspace and return on investment (ROI) for the huge initial investment on the UUV and its localisation hinder the R&D work and adoption of the autonomous UUV deployment on an industrial scale. The proposed system, which leverages the AprilTag (a fiducial marker used as a frame of reference) detection, provides cost-effective UUV localisation for the initial trials of autonomous UUV deployment, requiring only minor modifications to the aquaculture infrastructure. With such a cost-effective approach, UUV R&D engineers can demonstrate and validate the advantages and challenges of autonomous UUV deployment to farm operators, policymakers, and governing authorities to make informed decision-making for the future large-scale adoption of autonomous UUVs in aquaculture. Initial validation of the proposed cost-effective localisation system indicates that centimetre-level accuracy can be achieved with a single monocular camera and only 10 AprilTags, without requiring physical measurements, in a $115.46$ m³ laboratory workspace under various lighting conditions. Full article

To address the thermal management requirements of unmanned underwater vehicles (UUVs), this study designs a small high-speed water tunnel test section. Combining numerical simulations and experimental methods, we systematically investigate how outlet gauge pressure regulates flow structure and cooling performance from perspectives of vortex dynamics and turbulent energy scaling. Results demonstrate that increasing outlet pressure from 1.0 to 2.0 atm reduces system pressure loss by 26.60%, drag coefficient by 26.56%, and power consumption by 27.30%. The test section maintains flow uniformity below 1.0% with over 75% high-speed zone coverage, satisfying the ≥25 m/s design requirement. Mechanism analysis reveals that elevated pressure suppresses cavitation and boundary layer separation, attenuates large-scale vortex generation, and promotes turbulence transition to smaller scales, thereby optimizing energy transport and thermal uniformity. Experimental validation confirms the numerical model’s reliability in predicting flow characteristics, providing theoretical and technical support for advanced water tunnel design and battery thermal management optimization. Full article

This study investigates the dynamic formation reconfiguration problem for multi-UUV (multi-Unmanned Underwater Vehicle) systems, with a particular focus on the challenges posed by underwater acoustic communication. A two-dimensional grid model is established in the horizontal plane, taking the leader vehicle as a reference point. Based on this model, fundamental motion strategies for formation reconfiguration are proposed. To facilitate reconfiguration, the Particle Swarm Optimization (PSO) algorithm is utilized to assign desired position points to the follower UUVs within the new formation, enabling dynamic target point planning during reconfiguration. Furthermore, the process of generating motion guidance commands and the impact of acoustic communication delays during command transmission are analyzed. To address these delays, a fuzzy logic-based delay compensation method is proposed. Simulation experiments were conducted to validate the proposed approach. The results demonstrate that the formation reconfiguration planning method and the centralized command communication compensation strategy are both effective and practical for multi-UUV systems. Full article

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous navigation framework that integrates improved particle swarm optimization (PSO) for 3D global route planning, and a deep deterministic policy gradient (DDPG) algorithm enhanced by noisy networks and proportional prioritized experience replay (PPER) for local collision avoidance. To address dynamic sideslip and current-induced deviations during execution, a novel 3D adaptive line-of-sight (ALOS) guidance method is developed, which decouples nonlinear motion in horizontal and vertical planes and ensures robust tracking. The global planner incorporates a multi-objective cost function that considers yaw and pitch adjustments, while the improved PSO employs nonlinearly synchronized adaptive weights to enhance convergence and avoid local minima. For local avoidance, the proposed DDPG framework incorporates a memory-enhanced state–action representation, GRU-based temporal processing, and stratified sample replay to enhance learning stability and exploration. Simulation results indicate that the proposed method reduces route length by 5.96% and planning time by 82.9% compared to baseline algorithms in dynamic scenarios, it achieves an up to 11% higher success rate and 10% better efficiency than SAC and standard DDPG. The 3D ALOS controller outperforms existing guidance strategies under time-varying currents, ensuring smoother tracking and reduced actuator effort. Full article

This paper presents a Gaussian Process (GP)-based Funnel Model Predictive Control (MPC) for docking control of unmanned underwater vehicles (UUVs). The control method employs a Gaussian Process regression to learn the residual dynamics, which compensates for the unmodeled dynamics to improve prediction accuracy. Furthermore, a distance-adaptive performance funnel is designed to satisfy the field of view (FOV) constraints of sensors during the terminal guidance phase. The funnel imposes time-varying constraints on the UUV to ensure that the docking station remains observable. This funnel constraint is integrated into the cost function of the MPC, which systematically enforces safety without the computational complexity of traditional invariant sets. Comparative simulations validate the framework’s reliability under external disturbances, demonstrating superior tracking precision against conventional MPC benchmarks. Full article

To address the problem of platform scattering interference in direction finding using vector hydrophones mounted on unmanned underwater vehicle (UUV) platforms, this paper introduces a direction-finding error compensation method based on embodied transfer function (ETF) correction within the framework of embodied cognition theory. By establishing an analytical model of the scattered sound field of an infinite rigid cylinder, the influence mechanism of the UUV platform on the sound pressure and vibration velocity measurements of the vector hydrophone is systematically investigated, and the concepts of sound pressure ETF and vibration velocity ETF are defined. The research results indicate that at an operating frequency of 800 Hz, the ETF-based direction-finding method reduces the average direction-finding error from 8.8° to 6.2°, representing a performance improvement of 30.2%. Moreover, when the target lies near the transverse, the direction-finding error of the embodied model remains below 1.5°. This study provides novel theoretical support and a technical framework for achieving high-precision direction finding of vector hydrophones mounted on UUV platforms. Full article

Formation rendezvous is a critical phase during the deployment or recovery of multiple unmanned underwater vehicles (UUVs) in cooperative missions, and represents one of the core problems in multi-UUV cooperative planning. In practical marine environments with obstacles and currents, multiple constraints must be simultaneously satisfied, including obstacle avoidance, inter-UUV collision prevention, kinematic limitations, and specified initial and terminal states. These requirements make energy-optimal trajectory planning for multi-UUV formation rendezvous highly challenging. Traditional integrated cooperative planning methods often struggle to obtain optimal or even feasible solutions due to the complexity of constraints and the vastness of the solution space. To address these issues, a dual-layer planning framework for multi-UUV formation rendezvous trajectory planning in environments with obstacles and currents is proposed in this paper. The framework consists of an initial individual trajectory planning layer and a secondary cooperative planning layer. In the initial individual trajectory planning stage, the Grey Wolf Optimization (GWO) algorithm is employed to optimize high-order terms of polynomial curves, generating initial trajectories for individual UUVs that satisfy obstacle avoidance, kinematic constraints, and state requirements. These trajectories are then used as inputs to the secondary cooperative planning stage. In the cooperative stage, a Self-Adaptive Particle Swarm Optimization (SAPSO) is introduced to explicitly address inter-UUV collision avoidance while incorporating all individual constraints, ultimately producing a cooperative rendezvous trajectory that minimizes overall energy consumption. To validate the effectiveness of the proposed method, a simulation environment incorporating vortex flow fields and real-world island topography was constructed. Simulation results demonstrate that the proposed hierarchical trajectory planning method is capable of generating energy-optimal formation rendezvous trajectories that satisfy multiple constraints for multi-UUV systems in environments with obstacles and ocean currents, highlighting its strong potential for practical engineering applications. Full article

This work is devoted a particular trajectory tracking problem of underactuated unmanned underwater vehicles (UUVs) with model reduced to five degrees of freedom (DOF). Such a model is quite widespread in the literature and used to describe the dynamics of UUVs. On this basis, various control strategies are designed, such that the closed-loop system track the trajectory with assumed accuracy. Unfortunately, the main drawback of this approach is that the presented results relate to the situation when the center of the mass is the same as the geometric center-point. Several algorithms have been selected for testing the control effectiveness (one based on the model with shifted center of the mass and other four based on the assumption that this center is in the same place as the geometric center). The goal of the paper is to check whether the correction mechanisms contained in the controller ensure the implementation of the trajectory tracking task. Simulation results on the five-DOF vehicle model show performance of the considered control schemes in the presence of a small shift of the mass center. Full article

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain. Full article

Unmanned Underwater Vehicle (UUV) swarms have become increasingly crucial for underwater exploration and applications, where their coordinated operation offers significant advantages over single-vehicle systems. However, unlike single-vehicle systems, the development of swarm control systems is more complicated, especially because there are limited integrated toolchains that can cover both global scheme design and individual vehicle implementation. Engineers may have to develop a global scheme and then partition it manually for individual vehicle implementation, which can result in substantial efficiency losses. To address this difficulty, an integrated experimental framework is developed to support the complete workflow of UUV swarm control development, from unified algorithm design and system simulation to automated code generation and individual deployment. The architecture of the proposed platform incorporates three principal elements: a global simulation environment that enables virtual validation of swarm collective behavior, a rapid prototyping module that facilitates code generation/partitioning and individual implementation, and a digital twin visualization component that provides real-time monitoring capabilities. A case study demonstrates that the platform can integrate global design with individual implementation. In a comparative experiment where the same engineering team implemented a three-UUV formation control algorithm, the use of our platform reduced the time from algorithm design to successful deployment from an estimated 6 h (using manual coding and integration) to under one hour, representing about an 80% reduction in development time. Full article

This study addresses autonomous underwater vehicle (UUV) recovery onto dynamic docking stations by proposing a fork-column recovery control system with a segmented docking strategy (long-distance approach + guided descent). To enhance model fidelity, transmission lag of actuators is captured by a specified transfer function, and nonlinear dynamics are characterized as an improved quasi-linear parameter-varying (QLPV) model. An adaptive variable–prediction–step mechanism was designed to accommodate different phases of acoustic–optical guided recovery. A model predictive controller (MPC) was developed based on an improved dynamic model to effectively handle complex constraints during the recovery process. Simulation and physical experiments demonstrated that the proposed system significantly reduces errors, among which the control accuracy (tracking error under disturbance < 0.3 m) and docking success rate (>95%) are notably superior to traditional methods, providing a reliable solution for the dynamic recovery of unmanned underwater vehicles (UUVs). Full article

In response to the problem of decreased collaborative control performance in underwater unmanned vehicles (UUVs) with communication packet loss, a GRU-KF method for multi-UUV control that integrates a gated recurrent unit (GRU) and a Kalman filter (KF) is proposed. First, a UUV feedback linearization model and a current model are established, and a multi-UUV controller-based leader–follower method is designed, using a neural network-based radial basis function (RBF) to counteract the uncertainty effects in the model. For scenarios involving packet loss in multi-UUV collaborative communication, the GRU network extracts historical temporal features to enhance the system’s adaptability to communication uncertainties, while the KF performs state estimation and error correction. The simulation results show that, compared to compensation by the GRU network, the proposed method significantly reduces the jitter level and convergence time of errors, enabling the formation to exhibit good robustness and accuracy in communication packet loss scenarios. Full article

For multiple unmanned underwater vehicles (UUVs) systems, obstacle avoidance during cooperative operation in complex marine environments remains a challenging issue. Recent studies demonstrate the effectiveness of deep reinforcement learning (DRL) for obstacle avoidance in unknown marine environments. However, existing methods struggle in marine environments with complex non-convex obstacles, especially during multi-UUV cooperative operation, as they typically simplify environmental obstacles to convex shapes with sparse distributions and ignore the dynamic coupling between cooperative operation and collision avoidance. To address these limitations, we propose a centralized training with decentralized execution framework with a novel multi-agent dynamic encoder based on an efficient self-attention mechanism. The framework, to our knowledge, is the first to dynamically process observations from an arbitrary number of neighbors that effectively addresses multi-UUV collision avoidance in marine environments with complex non-convex obstacles while satisfying additional constraints derived from cooperative operation. Experimental results show that the proposed method effectively avoids obstacles and satisfies cooperative constraints in both simulated and real-world scenarios with complex non-convex obstacles. Our method outperforms typical collision avoidance baselines and enables policy transfer from simulation to real-world scenarios without additional training, demonstrating practical application potential. Full article