Search Results (180)

To address the limited endurance of unmanned underwater vehicles (UUVs) during long-duration missions, this study proposes a foldable and retractable wave energy converter (WEC) conformally integrated with the UUV hull. A two-degrees-of-freedom heave-coupled dynamic model of the float–UUV system is established, and parameter-matching relationships are derived using complex dynamic stiffness and impedance-matching theory. A bidirectionally coupled STAR-CCM+-AMESim co-simulation framework resolves the nonlinear viscous flow field, relative motion, and PTO dynamic response in closed loop. Under regular wave conditions defined based on a representative Bohai Sea state, the effects of the transmission ratio and spring stiffness on the coupled motion and equivalent resistive load power output are systematically investigated. Under the specified wave condition, average electrical power varies unimodally with both parameters, reaching 70.8 W at a transmission ratio of 15 and a spring stiffness of 4642 N/m; the corresponding peak power is 161.2 W. The system is more sensitive to increases than decreases in transmission ratio, suggesting a value slightly below the theoretical optimum for engineering design. The instantaneous power shows an asymmetric double-peak pattern, indicating a shift in dominance between direct float-driven generation and spring-mediated energy release. Agreement between theory and co-simulation provides numerical cross-validation and offers a theoretical basis and numerical methodology for designing and optimizing WECs on mobile UUV platforms. Full article

Unmanned Underwater Vehicle (UUV) swarms operating in complex marine environments must accurately assess threats from surrounding targets to ensure mission success and navigational safety. However, existing threat assessment methods face three fundamental bottlenecks when applied to underwater swarms: the inability to track temporally evolving target intentions, reliance on subjective indicator weighting for multi-target ranking, and vulnerability to spatially heterogeneous sonar noise. This paper proposes a hierarchical threat assessment framework that addresses these bottlenecks through three integrated modules. First, a Dynamic Bayesian Network with a specially designed heading factor tracks target intention over time, propagating threat probabilities across sequential observations and enabling early warning before the closest point of approach. Second, a Vieta’s theorem-based algebraic ranking algorithm constructs comprehensive threat vectors via elementary symmetric polynomials of six indicator utilities, avoiding explicit expert-defined weighting coefficients in the multi-attribute ranking stage while capturing both independent and synergistic indicator interactions. Third, a distance-weighted swarm aggregation strategy suppresses individual sonar noise by assigning higher fusion weights to geographically closer nodes, exploiting the spatial diversity inherent in swarm configurations. Simulation experiments under representative target-motion scenarios validate the framework across four complementary experimental studies. Results demonstrate that the DBN reduces output variance by over 56% compared to static Bayesian networks and responds to abrupt intention changes within 15 s. The algebraic ranking algorithm achieves identical prioritization to TOPSIS without requiring any manual or data-dependent weights. The distance-weighted aggregation reduces root mean square error by 63.2% and improves signal-to-noise ratio by 8.7 dB over equal-weight averaging. The proposed framework provides a principled and interpretable solution for simulation-based autonomous threat perception in representative underwater swarm scenarios. Full article

Underwater object detection is a core technology for environmental perception and autonomous operation of unmanned underwater vehicles (UUVs). However, optical and acoustic sensing alone suffer from physical limitations, leading to missed and false detections in turbid, low-light, or long-range conditions. To overcome these limitations, this paper develops an acoustic–optical multimodal fusion detection module (AOMFDM) tailored for UUV deployment. The module employs dual YOLOv5 models for separate processing of sonar and optical images. An interference source quantification estimation network is introduced to extract environmental degradation features, including noise, blur, illumination, contrast, and color cast. A heterogeneous feature map matching network and a deep sparse autoencoder are further designed to achieve cross-modal alignment and fusion of acoustic and optical features. Additionally, attention mechanisms, anchor-based box annotation, and weighted boxes fusion (WBF) are incorporated to enhance detection robustness. For model training and evaluation, we construct the Underwater Sonar Detection (USD) and Underwater Optical Detection (UOD) datasets, covering diverse water qualities, illumination levels, target materials, and interference scenarios. Experimental results demonstrate that, by exploiting the complementarity of acoustic and optical modalities together with adaptive alignment strategies, the proposed module significantly boosts both detection reliability and generalization capability for UUVs in challenging underwater environments. Full article

Driven by the engineering requirement for high acoustic stealth of unmanned underwater vehicles (UUVs), this paper investigates the electromagnetic vibration of an 8-pole, 48-slot, surface-mounted permanent magnet synchronous motor (SPMSM) employed in the propulsion system through multi-physics coupling analysis and experimental testing. First, analytical calculations of electromagnetic force waves are performed based on the Maxwell stress tensor method and the magnetomotive force–permeance method to analyze the spatial orders, temporal orders, and sources of the harmonics. Then, a two-dimensional motor model is established using ANSYS electromagnetic field simulation software to investigate the temporal and spatial characteristics of electromagnetic force waves under both no-load and on-load conditions. Fourier decomposition is applied to obtain the amplitude-frequency characteristics, thereby verifying the correctness of the analytical results. Subsequently, three-dimensional models of the stator core and the complete stator assembly are constructed in the physical field, and their modal frequencies and mode shapes are obtained through simulation. On this basis, harmonic response analysis is conducted by applying electromagnetic force waves to the stator teeth, and vibration simulations are performed in ANSYS Workbench to acquire vibration characteristics. Vibration experiments are then carried out at multiple rotational speeds, and the experimental results are compared with the simulation results to validate the feasibility and accuracy of the finite element modeling approach. Since the measured motor vibration results are influenced not only by electromagnetic excitation forces, but also by various factors such as mechanical structure, instrument installation, and fixture conditions, while the simulation model in this paper inevitably simplifies damping, housing details, inverter control effects, and considers only the effect of radial electromagnetic forces, there exists a certain discrepancy between the simulated and measured motor vibration acceleration results. However, the main vibration trends in the low-frequency range below 800 Hz are basically consistent, particularly at the second and fourth harmonic frequencies, where the vibrations are electromagnetic vibrations caused by radial electromagnetic force waves, with relative errors between the measured and simulated values of 18% and 25%, respectively. This finite element model can be used for preliminary design evaluation of PMSMs and rapid prediction of electromagnetic vibration, providing researchers with a convenient and practical research approach and methodology. Finally, by analyzing factors that may influence motor vibration, this paper proposes design modifications to the stator structure and air-gap width, providing an optimized solution for reducing electromagnetic vibration of the permanent magnet synchronous motor and avoiding resonance. Full article

Thruster health monitoring is essential for unmanned underwater vehicles (UUVs), where propulsion degradation can reduce manoeuvrability, tracking accuracy, and mission safety. Practical diagnosis remains difficult because labelled vibration samples are scarce and deployed vehicles may encounter fault states absent from the support library. Closed-set few-shot classifiers are unreliable in this setting because every query must be assigned to a known state. SAGE-PD (Spectral Attention-Guided Episodic Prototype Displacement) is a few-shot open-set method for UUV thruster vibration monitoring. It encodes each vibration window through raw-waveform and STFT branches, constructs episode-specific prototypes for known thruster states, and models their relational geometry. Unknown-state evidence is obtained by replacing the predicted prototype with the query embedding and measuring the displacement of the transformed prototype structure. Spectral attention weights this displacement toward thruster-related time–frequency components. On the analysed UUV thruster dataset, SAGE-PD achieved $0.9078\pm 0.0573$ Open OA and $0.9011\pm 0.0407$ AUROC in a 5-shot evaluation, and $0.8745\pm 0.0457$ Open OA and $0.8953\pm 0.0409$ AUROC in a 1-shot evaluation. The results show that SAGE-PD improves both known-state recognition and unknown-state rejection by combining support-structure compatibility with vibration-aware spectral evidence. Full article

Cooperative pursuit of multiple unmanned underwater vehicles (UUVs) is a typical adversarial task in the fields of underwater security and target interception. However, inherent underwater challenges such as communication delay, ocean-current disturbances, limited sonar detection range, randomly distributed obstacles, and maneuvering evasive targets may lead to state information lag, difficulty in cooperative decision-making, unstable pursuit formation, and complex dynamic obstacle avoidance. To tackle the above issues, this paper establishes a hierarchical UUV model that combines horizontal-plane kinematics, simplified surge-yaw dynamics, ocean-current disturbance terms, and low-level actuator saturation constraints within a deep reinforcement learning framework. Secondly, a PER-TD3-based escape strategy learning method is proposed to construct a maneuverable evasive target model; the trained evader maps local sonar, pursuer-relative, obstacle, and current observations into bounded speed and yaw-rate commands, thereby providing an adversarial target for subsequent pursuit training. Thirdly, aiming at the decision-making lag caused by communication delay and local observation, an LSTM-PMADDPG single-target pursuit method is presented as a temporal baseline. Finally, in view of the interference of dynamic obstacles and the difficulty of multi-target assignment, an LSTM-MATD3 multi-target pursuit algorithm is constructed to realize autonomous multi-target allocation, dynamic obstacle avoidance, and stable encirclement. Full article

The advancing perception capabilities of an individual unmanned underwater vehicle (UUV) pose new challenges for multi-target perceptual consistency in underwater bearing-only tracking (BOT) systems. Accurate target state estimation necessitates two key prerequisites: sensor bias compensation and precise data association. The biases encompass both sensor spatial bias and signal propagation delay between the target and sensor. This paper introduces a measurement model that explicitly accounts for these factors. To address the lack of prior target information, an initial target state estimation algorithm is developed based on maximum likelihood estimation (MLE), with a refined bio-inspired variant incorporating particle swarm optimization (PSO). To this end, a cost function is formulated to transform the BOT data association problem into an assignment problem. Thereafter, an iterative multi-target data association (MDA) algorithm, integrated with the expectation-maximization (EM) method, is designed to jointly mitigate the effects of signal delay and spatial bias. Monte Carlo simulation scenarios validate the overall effectiveness of the proposed MDA framework. Specifically, the EM-based spatial bias estimation method demonstrates accurate bias estimation capability. Full article

Cooperative encirclement using multiple unmanned underwater vehicles (UUVs) is a critical task in underwater base defense, yet existing approaches typically rely on static risk rules that cannot fuse multi-source information dynamically and decouple coordination-point allocation from live risk assessment, limiting adaptive response in evolving adversarial conditions. To address these limitations, this paper proposes a continuous risk-driven adaptive weighting strategy for coordination-point allocation, which dynamically adjusts evaluation metric weights as a smooth function of real-time Bayesian risk intensity, enabling seamless transitions between time-efficiency-oriented and synchronization-oriented encirclement modes without discrete switching. This strategy is embedded within a closed-loop decision framework that integrates a Bayesian network for dynamic risk quantification and an online corner-based deployment model to guarantee continuous execution feasibility from approach to final task completion. Simulations validate that the proposed framework ensures stable, adaptive encirclement as risk levels evolve dynamically throughout the mission. Compared to the greedy priority-based allocation and the time-balance allocation as conventional baselines, the proposed strategy reduces task completion time by up to 22.8% and 9.5%, respectively, demonstrating strong robustness in dynamic, uncertain environments. Full article

Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework’s efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks. Full article

Large Unmanned Underwater Vehicles (UUVs) are playing an increasingly critical role in complex marine missions due to their enhanced payload and endurance capabilities. However, the safe recovery of these platforms remains a significant challenge, complicated by their high inertia, strong hydrodynamic interactions, and unpredictable environmental disturbances. In particular, the nonlinear coupling effects between the mechanical structure and the hydrodynamic environment exert a considerable influence on the system, accounting for nearly 50% of the tension on the arresting cable. To address these challenges, this paper proposes a variable tension control strategy for a UUV underwater arresting recovery system, utilizing a Well-Shaped Reward Entropy-regularized Proximal Policy Optimization (WSR-E-PPO) algorithm. In this framework, the real-time velocity and displacement of the UUV are utilized to represent the spatiotemporal characteristics of the recovery state, and a hybrid reward function integrating sparse and continuous rewards based on Potential-Based Reward Shaping (PBRS) is designed. Simulation results demonstrate that the proposed method enables the UUV to return to the docking point without oscillation, while effectively limiting the total recovery time to approximately 80 s—a 37.5% reduction compared with existing methods. Furthermore, the strategy ensures smoother tension regulation throughout the process. These findings provide a solid technical foundation and assurance for the stable and safe underwater recovery of large UUVs. Full article

Three-dimensional (3D) path planning for Unmanned Underwater Vehicles (UUVs) in typical marine operating conditions presents high-dimensional, non-convex optimization challenges due to undulating seabed topography, underwater threat sources, and coupled multi-physical constraints. Existing studies lack multi-strategy collaborative optimization mechanisms specifically designed for UUV 3D marine navigation constraints, thereby hindering the simultaneous achievement of real-time performance, safety, and energy efficiency in path planning. This paper first develops a comprehensive multi-dimensional cost function based on the dynamic characteristics of UUV underwater 3D navigation, operational rules for typical marine operating conditions, and safe navigation requirements through mathematical modeling, thereby formally transforming the UUV 3D path planning problem in typical marine operating conditions into a multi-constrained nonlinear global optimization problem. To address this challenge, an Adaptive Cooperative WMA (ACWMA) is proposed. The key improvements include: (i) an adaptive parameter switching and Lévy flight disturbance mechanism to balance exploration and exploitation capabilities; (ii) an optimal value leadership strategy to accelerate convergence; and (iii) a team collaborative learning mechanism to enhance population optimization efficiency. Algorithm benchmark performance is validated using the CEC 2017 standard test suite, while comparative and ablation experiments are conducted in multi-gradient complex marine 3D scenarios. The statistical significance of the algorithm performance improvement is verified using the Wilcoxon rank-sum test. The proposed ACWMA achieves a significant performance improvement of 8.71% over the suboptimal WMA in terms of core performance metrics and generates low-energy-consumption 3D paths that satisfy multiple constraints. These findings provide valuable engineering insights for 3D path planning in UUV autonomous operations within typical marine operating conditions. Full article

This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following functional areas: environment perception, simultaneous localization and mapping (SLAM), collision avoidance and path planning, and motion control. Different ML methods are covered, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning and deep reinforcement learning. The reviewed methods are analyzed with respect to their performance, robustness, and suitability for different operational environments, including underwater, surface, air, and land domains. Finally, the authors identify key challenges and outline promising future directions aimed at improving the safety, autonomy, and reliability of autonomous vehicles. Full article

Cooperative motion control is a fundamental requirement for unmanned underwater vehicle (UUV) swarms operating in complex marine environments. Conventional swarm motion-control algorithms may suffer from limited convergence efficiency and redundant obstacle-avoidance maneuvers when the swarm is required to move toward multiple task-related regions. To address these issues, this study proposes a Vicsek-based distributed motion-control framework with static summoning points and threat-selective obstacle avoidance. First, static summoning points are introduced as predefined task-attraction locations, and a movement-cost-based assignment rule is used to divide the initially mixed swarm into task-oriented subclusters. Under a limited field-of-view constraint, a summoning factor is incorporated into the heading-update rule to balance local neighbor alignment and directional guidance toward the assigned summoning point. Then, an obstacle-avoidance strategy is developed by considering both the relative position of obstacles and the velocity direction of individuals. The detected obstacles are classified as current obstacles or potentially threatening obstacles, and avoidance maneuvers are triggered only when a current obstacle lies within the prescribed safety distance. Simulation results demonstrate that the proposed VSSPAO framework can improve convergence consistency, reduce convergence time, and decrease redundant obstacle-avoidance routes compared with the reference algorithms. The proposed method provides an interpretable and computationally simple distributed coordination mechanism for UUV swarm segmentation, task-oriented aggregation, and obstacle avoidance. Full article

Nearshore environments, characterized by complex obstacle distributions and dynamic disturbances, pose significant challenges to global path planning for unmanned underwater vehicles (UUVs). To address these challenges, this paper proposes an improved artificial potential field-guided RRT* (IAPF-RRT*) method for efficient and robust path planning in coastal environments. The proposed approach integrates an improved artificial potential field into the sampling and node expansion processes to enhance goal-directed exploration and obstacle avoidance capability. In addition, a target-biased sampling strategy and an adaptive attraction mechanism are introduced to accelerate convergence, while a NURBS-based refinement scheme is employed to improve trajectory continuity and smoothness. Extensive simulations in representative scenarios demonstrate that the proposed method significantly improves planning efficiency, reducing planning time by up to 80% compared with conventional RRT-based methods, while substantially decreasing redundant node expansion and improving trajectory smoothness. A consistently high success rate is maintained across all scenarios. Field experiments in nearshore environments further validate the robustness and practical applicability of the proposed method. These results indicate that the proposed IAPF-RRT* method achieves a favorable balance between efficiency, robustness, and path quality, making it well-suited for real-world UUV operations in complex nearshore environments. Full article

Under hull–propulsor coupling conditions, the geometric shape of an unmanned underwater vehicle (UUV) can significantly affect the inflow conditions of the aft rim-driven thruster (RDT) and, consequently, its propulsive performance. However, the number of UUV shape design parameters is relatively large, and their influences on the propulsive efficiency of the RDT differ markedly. If an equal-weight search strategy is still adopted for optimization, the computational cost will increase and the optimization efficiency will be reduced. To address this issue, this paper proposes an efficient global-sensitivity-information-driven sequential surrogate-based optimization method for the shape optimization design of the UUV, with the aim of improving the propulsive efficiency of the RDT corresponding to the self-propulsion equilibrium state under the cruise condition. Based on the hull–propulsor coupled numerical model of the UUV and RDT, the proposed method obtains the propulsive efficiency of the RDT at the self-propulsion point under the cruise condition by solving the self-propulsion equilibrium condition. On this basis, Sobol global sensitivity analysis is performed using the Kriging surrogate model to quantitatively evaluate the influence of the UUV shape design parameters on the propulsive efficiency of the RDT. Then, the global sensitivity information is mapped into optimization weights. Based on this, the minimum of surrogate prediction (MSP) and expected improvement (EI) sampling criteria are introduced. In this way, a surrogate model sequential optimization method driven by global sensitivity information is developed. The optimization results show that, after optimizing the UUV external shape, the propulsive efficiency of the RDT under the cruise condition is increased by 22.83%, thereby verifying the effectiveness of the proposed method. Full article