Search Results (35)

Unmanned underwater vehicles (UUVs) play a pivotal role in marine applications such as resource exploration, maritime search and rescue. However, communication signal loss remains a critical bottleneck, constraining UUV autonomous operation and mission reliability across four dimensions: navigation, coordination, monitoring, and planning. To address these challenges in communication-denied environments, this paper proposes a UUV digital twin system utilizing motion prediction technology, such as virtual mapping, prediction, and autonomous decision support. Based on a four-layer architecture—comprising the Physical Entity Layer, Virtual Entity Layer, Twin Data & Connectivity Layer, and Services Layer, the system achieves full-state mapping and real-time visualization. Specifically, a hybrid prediction model integrating Transformer and Convolutional Neural Networks (CNN) architectures is developed to extract multi-scale features for resistance prediction, which serves as the critical basis for UUV motion state forecasting. Experimental validation confirms the system’s capability for real-time resistance tracking and high-precision prediction, providing a robust foundation for autonomous navigation control and energy management. These results advance the development of specialized UUV digital twin systems and establish a robust foundation for their engineering applications. 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

This paper addresses the issue of low detection accuracy in underwater optical images for unmanned underwater vehicles (UUVs) during practical operations, caused by factors such as uneven lighting, blur, complex backgrounds, and target occlusion. To enhance the autonomous perception and control capabilities of UUVs, a high-precision algorithm named MBACA-YOLO is proposed based on the YOLOv13n model. Firstly, the convolutional layers in the backbone network of YOLOv13n are optimized by replacing stride-2 convolutions with stride-1 and embedding SPD layers to enable richer feature extraction. Secondly, the newly proposed MBACA attention mechanism is integrated into the final layer of the backbone network, enhancing effective features and suppressing background noise interference. Thirdly, traditional upsampling in the neck network is replaced with CARAFE upsampling to mitigate noise pollution. Finally, an Alpha-Focal-CIoU loss function is designed to improve the accuracy of bounding box regression for underwater targets. To validate the algorithm’s effectiveness, experiments were conducted on the URPC dataset with the following evaluation protocol: 640 × 640 input resolution, batch size 1, FP32 precision, and standard NMS. All results are from a single random seed with 300 epochs of training. The proposed MBACA-YOLO algorithm outperforms the baseline YOLOv13n model, improving mAP@0.5 and mAP@0.5:0.95 by 3.1% and 2.8% respectively, while adding only 0.49M parameters and 1.0 GFLOPs, with an FPS drop of just 2 frames. This makes it an efficient, deployable perception solution for automated Unmanned Underwater Vehicles (UUVs), significantly advancing intelligent underwater systems. 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

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

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

Underwater target detection and segmentation in Side-Scan Sonar (SSS) imagery is challenged by low signal-to-noise ratios, geometric distortions, and Unmanned Underwater Vehicles (UUVs)’ computational constraints. This paper proposes CKAN-YOLOv8, a lightweight multi-task network integrating Kolmogorov–Arnold Networks Convolution (KANConv) into YOLOv8. The core innovation replaces conventional convolutions with KANConv blocks using learnable B-spline activations, dynamically adapting to noise and multi-scale targets while ensuring parameter efficiency. The KANConv-based Path Aggregation Network (KANConv-PANet) mitigates geometric distortions through spline-optimized multi-scale fusion. A dual-task head combines CIoU loss-driven detection and a boundary-sensitive segmentation module with Dice loss. Evaluated on a dataset (50 raw images augmented to 2000), CKAN-YOLOv8 achieves state-of-the-art performance as follows: 0.869 AP@0.5 and 0.72 IoU, alongside real-time inference at 66 FPS. Ablation studies confirm the contributions of KANConv modules to noise robustness and multi-scale adaptability. The framework demonstrates exceptional robustness to noise, scalability across target sizes. Full article

This paper proposes a novel SDA-Mask R-CNN framework for precise seabed terrain edge feature extraction from Side-Scan Sonar (SSS) images to enhance Unmanned Underwater Vehicle (UUV) perception and navigation. The developed architecture addresses critical challenges in underwater image analysis, including low segmentation accuracy and ambiguous edge delineation, through three principal innovations. First, we introduce a Structural Synergistic Group-Attention Residual Network (SSGAR-Net) that integrates group convolution with an enhanced convolutional block attention mechanism, complemented by a layer-skipping architecture for optimized information flow and redundancy verification for computational efficiency. Second, a Depth-Weighted Hierarchical Fusion Network (DWHF-Net) incorporates depthwise separable convolution to minimize computational complexity while preserving model performance, which is particularly effective for high-resolution SSS image processing. This module further employs a weighted pyramid architecture to achieve multi-scale feature fusion, significantly improving adaptability to diverse object scales in dynamic underwater environments. Third, an Adaptive Synergistic Mask Optimization (ASMO) strategy systematically enhances mask generation through classification head refinement, adaptive post-processing, and progressive training protocols. Comprehensive experiments demonstrate that our method achieves 0.695 (IoU) segmentation accuracy and 1.0 (AP) edge localization accuracy. The proposed framework shows notable superiority in preserving topological consistency of seabed features, offering a reliable technical framework for underwater navigation and seabed mapping in marine engineering applications. Full article

This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of mAP@0.5 to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in mAP@0.5. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking. Full article

Accurate and stable estimation of the position and trajectory of noncooperative targets is crucial for the safe navigation and operation of sonar-equipped underwater unmanned vehicles (UUVs). However, the uncertainty associated with sonar observations and the unpredictability of noncooperative target movements often undermine the stability of traditional Bayesian methods. This paper presents an innovative approach for noncooperative target state estimation utilizing 3D Convolutional Kolmogorov–Arnold Networks (3DCKANs). By establishing a non-Markovian model that characterizes state estimation of UUV noncooperative targets under uncertain observations, we leverage historical data to construct 3D Convolutional Kolmogorov–Arnold Networks. This network learns the patterns of sonar observations and target state transitions from a substantial offline dataset, allowing it to approximate the posterior probability distribution derived from past observations effectively. Additionally, a sliding window technique is integrated into the convolutional neural network to enhance the estimator’s fault tolerance with respect to observation data in both temporal and spatial dimensions, particularly when posterior probabilities are unknown. The incorporation of the Kolmogorov–Arnold representation within the convolutional layers enhances the network’s capacity for nonlinear expression and adaptability in processing spatial information. Finally, we present statistical experiments and simulation cases to validate the accuracy and stability of the proposed method. Full article

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment. Full article

The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio. Full article

Underwater optical wireless communication (UOWC) is a field of research that has gained popularity with the development of unmanned underwater vehicle (UUV) technologies. Its utilization is crucial in offshore industries engaging in sustainable alternatives for food production and energy security. Although UOWC can meet the high data rate and low latency requirements of underwater video transmission for UUV operations, the links that enable such communication are affected by the inhomogeneous light attenuation and the presence of sunlight. Here, we present how the underwater spectral distribution of the light field can be modeled along the depths of eight stratified oceanic water types. We considered other established models, such as SPCTRL2, Haltrin’s single parameter model for inherent optical properties, and a model for the estimation of the depth distribution of chlorophyll-a, and present insights based on transmission wavelength for the maximum signal-to-noise ratio (SNR) under different optical link parameter combinations such as beam divergence and transmit power under “daytime” and “nighttime” conditions. The results seem to challenge the common notion that the blue-green spectrum is the most suitable for underwater optical communication. We highlight a unique relationship between the transmission wavelength for the optimal SNR and the link parameters and distance, which varies with depth depending on the type of oceanic water stratification. Our analyses further highlighted potential implications for solar discriminatory approaches and strategies for routing in cooperative optical wireless networks in the photic region. Full article

The widespread use of Unmanned Underwater Vehicles (UUVs) in seafloor observatory networks highlights the need for docking stations to facilitate rapid recharging and effective data transfer. Floating docks are promising due to their flexibility, ease of deployment, and recoverability. To enhance understanding and optimize UUV docking with floating docks, we employ dynamic fluid body interaction (DFBI) to construct a seabed moored suspended dock (SMSD) model that features a guiding funnel, a suspended body, and a catenary of a mooring chain. This model simulates SMSD equilibrium stabilization in various ocean currents. Then, a UUV docking model with contact coupling is developed from the SMSD model to simulate the dynamic contact response during docking. The accuracy of the docking model was validated using previous experimental data. Through investigation of the UUV docking response results, sensitivity studies relating to volume, moment of inertia, mass, and catenary stiffness were conducted, thereby guiding SMSD optimization. Finally, sea tests demonstrated that the SMSD maintained stability before docking. During docking, the SMSD’s rotation facilitated smooth UUV entry. After the UUV docked, the SMSD was restored to its original azimuth, confirming its adaptability, stability, and reliability. Full article