Search Results (107)

Tethered unmanned surface vehicle (USV) and remotely operated vehicle (ROV) systems are widely used in deep-sea inspection, observation, and intervention tasks. During cooperative operations, the USV must follow the mission trajectory of the ROV while avoiding surface obstacles and maintaining a prescribed tether-related safety envelope. This study proposes a multi-objective nonlinear model predictive control (NMPC) framework for USV-side cooperative tracking and dynamic obstacle avoidance in tethered USV-ROV operations. The framework integrates the predicted three-dimensional ROV trajectory, USV nonholonomic motion, surface-obstacle avoidance, straight-line tether-length-related geometric constraints, and control-smoothness regulation into a unified receding-horizon optimization problem. Sequential Least Squares Programming is used to compute the online control sequence. Numerical simulations include obstacle-free tracking under bounded ocean-current disturbances and heterogeneous surface–underwater obstacle scenarios. The results show that the proposed controller provides improved tracking performance and maintains the straight-line tether-length proxy below the prescribed limit in the tested simulations. The current-disturbance results further indicate preliminary disturbance-rejection capability under bounded time-varying ocean currents. The study provides a controller-level numerical framework for cooperative tracking and surface obstacle avoidance in tethered USV-ROV operations. Full article

This study presents a simulation-based multiphysics design, modeling, and adaptive control framework for a dual-propulsion unmanned aerial underwater vehicle intended for aerial, near-surface, and fully submerged operation. The proposed platform uses four aerial rotors for flight and six underwater thrusters for submerged maneuvering, allowing medium-dependent actuation in air and water. Separate aerial and underwater six-degrees-of-freedom models are formulated and connected through a smooth altitude-dependent coordination strategy for the simplified near-surface region. Computational fluid dynamics is used to estimate submerged drag forces, while finite element analysis evaluates pressure-hull structural integrity at a depth of 20 m. At 0.2 m/s, the predicted horizontal and vertical drag forces are 1.62 N and 3.92 N, corresponding to quadratic damping coefficients of 40.5 and 98.0 N·s²/m². The FEA results show that PMMA provides a safety factor of 7.8, with a maximum displacement of 0.53 mm under hydrostatic loading. An adaptive backstepping controller with projected gain tuning, disturbance compensation, and constrained actuator allocation is developed. MATLAB/Simulink simulations demonstrate bounded trajectory tracking under nominal conditions, 20% parametric uncertainty, modeled disturbances, and a 0.5 m/s ocean current. Full article

With the rising trend of replacing synchronous generators with inverter-based resources, the grid inertia, frequency control, voltage stability, and fault ride-through are compromised. The current research focuses on the coordinated control of Voltage Source Converter-based HVDC (VSC HVDC) and Battery Energy Storage Systems (BESS) for improving the grid stability in the presence of intermittent sources. Two models are created in the MATLAB/Simulink 2025a environment: one for the grid-connected PV system with the addition of BESS in grid-forming mode (GFM) and grid-following mode (GFL), and the other for the multi-terminal HVDC system with the integration of wind energy from the ocean. The results show that the grid-forming converters perform better than grid-following converters in the event of disturbances, and the coordinated control structure aligns with the IEEE 2800-2022 for low-inertia grids. Full article

This study proposes an integrated task–path cooperative optimization method to address the suboptimal solutions caused by decoupled task allocation and path planning for grouped multi-USV formations. First, an integrated optimization model is established within a hierarchical dynamic closed-loop framework, incorporating a persistent ocean current disturbance of 0.12 m/s to ensure practical environmental realism. Furthermore, efficient solution algorithms are developed: an enhanced Hungarian algorithm for task allocation and a Sine Cosine Algorithm-optimized Artificial Potential Field (SCA-APF) method to resolve local minima. The simulation results demonstrate that the proposed method reduces the weighted total cost by 11.1% and improves task allocation efficiency by over 80.5% compared to improved genetic algorithms. In dynamic environments, the framework achieves an over 99% task completion rate. Crucially, the system maintains real-time responsiveness with per-step computation times below 0.1 s even for a swarm size of N = 32, proving its scalability and suitability for large-scale maritime coordination. Full article

Real-time path planning for Unmanned Surface Vehicles (USVs) in complex marine environments remains challenging due to unstructured environments, ocean current disturbances, and dynamic obstacles. This paper proposes an improved Hybrid Safety and Reward-Sensitive Twin Delayed Deep Deterministic Policy Gradient (H_RS_TD3) algorithm and constructs a high-fidelity simulation environment based on GEBCO bathymetric data and CMEMS ocean current data. The path planning problem is formulated as a Markov Decision Process (MDP), where the state space incorporates multi-beam radar perception, ocean current disturbances, and relative goal information, while the action space outputs continuous thrust and rudder commands subject to vehicle dynamics constraints. The proposed framework integrates a risk-aware hybrid safety decision architecture, a Trajectory Predictor Network (TPN), a Curvature-driven Advantage-based Prioritized Experience Replay (CDA-PER) mechanism, and an uncertainty-aware conservative Q-learning strategy to enhance navigation safety, sample efficiency, and policy stability. Comprehensive simulations demonstrate that, compared with baseline deep reinforcement learning methods, the proposed approach achieves faster convergence, improved stability, and competitive path efficiency while consistently maintaining sufficient obstacle clearance and millisecond-level inference latency, validating its effectiveness and practical feasibility for safe USV navigation in realistic dynamic marine environments. Full article

Accurate identification of hydrodynamic parameters is essential for high-fidelity modeling and control of unmanned underwater vehicles (UUVs). Compared with towing tank experiments and computational fluid dynamics simulations, system identification based on free-running trial data offers a cost-effective and scalable alternative. However, in real ocean environments, unmodeled lumped disturbances—such as shear currents, stratification-induced buoyancy variations, and wave-induced drift forces—strongly couple with the vehicle’s intrinsic dynamics. Conventional least-squares estimators and physics-informed neural networks tend to absorb environmental effects into the physical parameters, leading to physically inconsistent estimates. To address this challenge, this paper proposes a physics-guided hybrid network (PG-HyNet) with input-domain structural decoupling. The architecture explicitly separates the intrinsic rigid-body dynamics from spatially varying environmental disturbances by assigning dynamics-related states to a physics-constrained branch and position-dependent variables to a residual disturbance branch. A staged training strategy is introduced to stabilize identification and suppress parameter drift during optimization. The framework is validated using high-fidelity simulations incorporating shear currents, density stratification, and wave drift effects, as well as real-world lake trial data. The results demonstrate that PG-HyNet significantly improves robustness against disturbance-induced parameter compensation, enabling physically consistent hydrodynamic parameter recovery while accurately capturing spatially varying environmental disturbance effects. Full article

In underwater reconnaissance and patrol, AUV has to sense and judge traversability in cluttered areas that include reefs, cliffs, and seabed infrastructure. A narrow sonar field of view, occlusion, and current-driven disturbances leave the vehicle with local, time-varying information, so decisions are made with incomplete and uncertain observations. A path-planning framework is built around two coupled components: spatiotemporal graph neural network prediction and conditional normalizing flow (CNF)-based probabilistic environment reconstruction. Forward-looking sonar and inertial navigation system (INS) measurements are fused online to form a local environment graph with temporal encoding. Cross-temporal message passing captures how occupancy and maneuver patterns evolve, which supports path prediction under dynamic reachability and collision-avoidance constraints. For regions that remain unobserved, CNF performs conditional generation from the available local observations, producing probabilistic completion and an explicit uncertainty output. Conformal calibration then maps model confidence to credible intervals with controlled miscoverage, giving a consistent probabilistic interface for risk budgeting. To keep pace with ocean currents and moving targets, edge weights and graph connectivity are updated online as new observations arrive. Compared with Informed Random Tree star (RRT*), D* Lite, Soft Actor-Critic (SAC), and Graph Neural Network-Probabilistic Roadmap (GNN-PRM), the proposed method achieves a near 100% success rate at 20% occlusion and maintains about an 80% success rate even under 70% occlusion. In dynamic obstacle scenarios, it yields about a 4% collision rate at low speeds and keeps the collision rate below 20% when obstacle speed increases to 3 m/s. Ablation studies further demonstrate that temporal modeling improves success rate by about 7.1%, CNF-based probabilistic completion boosts success rate by about 13.2% and reduces collisions by about 17%, while conformal calibration reduces coverage error by about 6.6%, confirming robust planning under heavy occlusion and time-varying uncertainty. Full article

With the increasing detection of micro- and nanoplastics (MNPs) in marine environments and the expanding body of related research, their environmental behavior and ecological effects have become central topics in marine environmental science. This review addresses the growing concern over MNP pollution in the marine realm, encompassing their primary sources, spatial accumulation and distribution, environmental transport and transformation dynamics, and ecotoxicological effects on marine organisms and ecosystems, as well as the ecological risks they pose within key habitats such as seagrass beds and coral reefs. We synthesize evidence on the biological impacts of MNPs, including oxidative stress, tissue accumulation, metabolic disturbances, and immune impairment, as well as the heightened risk of pathogen transmission facilitated by the so-called “Plastisphere”. Moreover, we explore the potential implications of MNP exposure on oceanic carbon cycling and net primary productivity. The reviewed literature suggests that MNPs are capable of long-range transport and progressive fragmentation into ultrafine particles, which are readily ingested and retained by a wide array of marine organisms, subsequently inducing toxicological effects and compromising both organismal health and ecological integrity. Such disturbances may undermine critical ecosystem services, including carbon sequestration capacity and food web stability. Finally, based on the current research landscape, we outline future research priorities: improving environmental detection and toxicological evaluation of MNPs, elucidating their long-term effects at the ecosystem scale, and investigating their interactions with co-occurring pollutants under complex, multi-stressor scenarios. These efforts are essential to support science-based assessment and effective management strategies for marine MNP pollution. Full article

Ocean current energy, owing to its predictability and stability, is regarded as an ideal power source for distributed marine observation networks and underwater intelligent equipment. However, conventional ocean current energy devices that rely on rigid turbines and electromagnetic generators generally suffer from high cut-in flow velocity, bulky size, high maintenance costs, and significant environmental disturbance, making them unsuitable for deep-sea, miniaturized, and long-duration power supply scenarios. These limitations highlight the urgent need for flexible and low-speed energy harvesters capable of autonomous, long-term operation. In recent years, nanogenerator technology has provided new opportunities for distributed and low-power ocean current energy harvesting. PENGs and TENGs can directly convert weak mechanical energy into electricity, enabling energy harvesting in small-scale and low-velocity flow fields. PENGs offer high durability and mechanical robustness, whereas TENGs exhibit superior output performance in low-speed and intermittent flows. This paper provides a comprehensive review of structural designs, material innovations, interface engineering, hybrid energy-conversion architectures, and power-management strategies for PENG- and TENG-based ocean current energy harvesters. Overall, future progress will rely on the integration of intelligent materials, multi-field coupling mechanisms, and system-level engineering strategies to achieve durable, scalable, and autonomous ocean current energy harvesting for distributed marine systems. Full article

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. Full article

To address the challenges faced by autonomous underwater vehicles (AUVs) in search and rescue missions—specifically, vulnerability to ocean current interference and low task efficiency in complex marine environments—this paper proposes an Improved Multi-objective Ant Colony Optimization (IMOACO) algorithm. By incorporating ocean current dynamics and energy constraints, a current-guided multi-objective evaluation function and state transition function are constructed to guide AUVs to preferentially follow downstream paths. On this basis, the entropy weight method is integrated to enhance the heuristic function and pheromone update strategy of the Ant Colony Optimization (ACO), and a dynamic priority strategy is employed to optimize the traversal sequence of multiple objectives. Grid-based simulations using real nautical charts and field trials with the “Xinghai 300R” AUV demonstrate that the proposed method significantly improves path smoothness and mission efficiency, with the IMOACO algorithm achieving a 34.7% increase in multi-objective search efficiency. The results indicate that this method is well-suited for multi-objective search and rescue missions in environments with strong ocean current disturbances, offering strong potential for practical engineering applications. Full article

This paper presents a robust trajectory-tracking control framework for underwater vehicles operating in a relative coordinate system. Unlike conventional methods that define trajectories in the world frame, the proposed approach formulates the control problem directly in a moving reference frame, enabling accurate motion control with respect to dynamic and drifting objects affected by environmental disturbances such as ocean currents and waves. This relative-space formulation is particularly advantageous for tasks including diver guidance, floating-object inspection, and docking, where the reference itself is nonstationary. A coordinate transformation is introduced to consistently express the vehicle dynamics in the relative frame. Based on the transformed dynamics, a Time Delay Control (TDC) law is applied to estimate unmodeled dynamics and external disturbances without requiring precise system parameters. Theoretical stability analysis shows that the stability condition of the proposed controller is consistent with that of conventional TDC, allowing similar gain-tuning procedures. Simulation results demonstrate that the proposed controller achieves robust and smooth trajectory tracking even when the reference frame undergoes motion induced by ocean currents. Full article

Hybrid underwater gliders (HUGs) combine buoyancy-driven gliding with propeller-assisted propulsion, offering extended endurance and enhanced mobility for complex underwater missions. However, precise depth control remains challenging due to system uncertainties, environmental disturbances, and inadequate adaptability of conventional control methods. This study proposes a novel optimized line-of-sight active disturbance rejection control (OLOS-ADRC) strategy for HUG depth tracking in the vertical plane. First, an Optimized Line-of-Sight (OLOS) guidance dynamically adjusts the look-ahead distance based on real-time cross-track error and velocity, mitigating error accumulation during path following. Second, a Tangent Sigmoid-based Tracking Differentiator (TSTD) enhances the disturbance estimation capability of the Extended State Observer (ESO) within the Active Disturbance Rejection Control (ADRC) framework, improving robustness against unmodeled dynamics and ocean currents. As a critical step before costly sea trials, this study establishes a high-fidelity simulation environment to validate the proposed method. The comparative experiments under gliding and hybrid propulsion modes demonstrated that OLOS-ADRC has significant advantages: the root mean square error (RMSE) for depth tracking was reduced by 83% compared to traditional ADRC, the root mean square error for pitch angle was decreased by 32%, and the stabilization time was shortened by 14%. This method effectively handles ocean current interference through real-time disturbance compensation, providing a reliable solution for high-precision HUG motion control. The simulation results provide a convincing foundation for future field validation in oceanic environments. Despite these improvements, the study is limited to vertical plane control and simulations; future work will involve full ocean trials and 3D path tracking. Full article

Oceans, which cover more than 70% of Earth’s surface, are home to vast biological and mineral resources. Deep-sea exploration encounters significant challenges due to harsh environmental factors, including low temperatures, high pressure, and complex hydrodynamic forces. These constraints have led to the widespread use of underwater robots as essential tools for deep-sea resource exploration and exploitation. Conventional underwater robots, whether rigid with fixed stiffness or fully flexible, fail to achieve the propulsion efficiency observed in biological fish. To overcome this limitation, researchers have developed adjustable stiffness mechanisms for robotic fish designs. This innovation strikes a balance between structural rigidity for stability and flexible adaptability to dynamic environments. By dynamically adjusting localized stiffness, these bio-inspired robots can alter their mechanical properties in real time. This capability improves propulsion efficiency, energy utilization, and resilience to external disturbances during operation. This paper begins by reviewing the evolution of underwater robots, from fixed-stiffness systems to adjustable-stiffness designs. Next, existing methods for stiffness adjustment are categorized into two approaches: offline component replacement and online real-time adaptation. The principles, implementation strategies, and comparative advantages of each approach are then analyzed. Finally, we identify the current challenges in adjustable-stiffness underwater robotics and propose future directions, such as advancements in intelligent sensing, autonomous stiffness adaptation, and enhanced performance in extreme environments. Full article