Search Results (40)

Swarm coverage by unmanned underwater vehicles (UUVs) is essential for inspection, environmental monitoring, and search operations, but remains challenging in three-dimensional domains under limited sensing and communication. Pheromone-based stigmergic coordination provides a low-bandwidth alternative to explicit communication, yet conventional single-field models are susceptible to depth-dependent sensing inconsistencies and multi-source signal interference. This paper introduces a dual-trail stigmergic coordination framework in which a virtual pheromone field encodes short-term motion cues while an auxiliary coverage trail records the accumulated exploration effort. UUV motion is guided by the combined gradients of these two fields, enabling more consistent behavior across depth layers and mitigating ambiguities caused by overlapping pheromone sources. At the macroscopic level, swarm evolution is modeled by a coupled system of partial differential equations (PDEs) describing vehicle density, pheromone concentration, and coverage trail. A Lyapunov functional is constructed to derive sufficient conditions under which perturbations around the uniform coverage equilibrium decay exponentially. Numerical simulations in three-dimensional underwater domains demonstrate that the proposed framework reduces coverage holes, limits redundant overlap, and improves robustness with respect to a single-pheromone baseline and a potential-field-based controller. These results indicate that dual-field stigmergic control is a promising and scalable approach for UUV coverage in constrained underwater environments. 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) 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

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

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

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

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

Cooperative operations of Unmanned Underwater Vehicles (UUVs) have extensive applications in fields such as marine exploration, ecological observation, and subsea security. Path planning, as a key technology for UUV autonomous navigation, is crucial for enhancing the adaptability and mission execution efficiency of UUVs in complicated marine environments. However, existing methods still have significant room for improvement in handling obstacles, multi-task coordination, and other complex problems. In order to overcome these issues, we put forward a task allocation and path planning method for UUVs. First, we introduce a task allocation mechanism based on an Improved Grey Wolf Algorithm (IGWA). This mechanism comprehensively considers factors such as target value, distance, and UUV capability constraints to achieve efficient and reasonable task allocation among UUVs. To enhance the search efficiency and accuracy of task allocation, a Circle chaotic mapping strategy is incorporated into the traditional GWA to improve population diversity. Additionally, a differential evolution mechanism is integrated to enhance local search capabilities, effectively mitigating premature convergence issues. Second, an improved RRT* algorithm termed GR-RRT* is employed for UUV path planning. By designing a guidance strategy, the sampling probability near target points follows a two-dimensional Gaussian distribution, ensuring obstacle avoidance safety while reducing redundant sampling and improving planning efficiency. Experimental results demonstrate that the proposed task allocation mechanism and improved path planning algorithm exhibit significant advantages in task completion rate and path optimization efficiency. 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 mainly investigates the fault-tolerant consensus problem in heterogeneous multi-agent systems. Firstly, a control model of a leader–follower heterogeneous multi-agent system (HMAS) composed of multiple unmanned aerial vehicles (UAVs), multiple unmanned surface vehicles (USVs), and multiple unmanned underwater vehicles (UUVs) is established. Then, for the fault-tolerant control (FTC) consensus problem of heterogeneous systems under partial actuator failures and interruption failures, an optimal FTC protocol for heterogeneous multi-agent systems based on the control allocation algorithm is designed. The derived optimal FTC protocol is applied to the heterogeneous system. The asymptotic stability of the protocol is proved by the Lyapunov stability theory. Finally, the effectiveness of the control strategy is verified through simulation tests. 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

In modern naval confrontation systems, adversarial underwater unmanned vehicles (UUVs) pose significant challenges, which are deployed on unmanned aerial vehicles (UAVs) due to their inherent mobility and positional uncertainty. Effective neutralization threats demand sophisticated coordination strategies between distributed agents under partial observability. This paper proposes a novel Knowledge-Enhanced Multi-Agent Deep Reinforcement Learning (MADRL) framework for coordinating UAV swarms against adversarial UUVs in asymmetric confrontation scenarios, specifically addressing three operational modes: area surveillance, summoned interception, and coordinated countermeasures. Our framework introduces three key innovations: (1) a probabilistic adversarial model integrating prior intelligence and real-time UAV sensor data to predict underwater trajectories; (2) a Multi-Agent Double Soft Actor–Critic (MADSAC) algorithm, addressing Red team coordination challenges. Experimental validation demonstrates superior performance over baseline methods in Blue target detection efficiency (38.7% improvement) and successful neutralization rate (52.1% increase), validated across escalating confrontation scenarios. Full article

Based on the Newton–Euler method, a multi-body coupling dynamics model of the separation process of underwater vehicles is established. The conditions of contact and detachment between the sub-vehicle and each group of elastic gaskets are analyzed in detail, and the elastic gasket constraint model is established to simulate the elastic contact and detachment process. Based on the Computational Fluid Dynamics (CFD) method, the hydrodynamic data of vehicles under different cases is calculated. In this context, a relatively accurate hydrodynamic database is established, where the hydrodynamic of the Unmanned Underwater Vehicle (UUV) is obtained through fitting, while those of the sub-vehicles are calculated using online interpolation. These provide conditions for realizing Fluid–Structure Interaction (FSI) calculation. Utilizing the FSI simulation method in the multi-body separation process, the separation dynamics of the multi-vehicle under the influence of elastic constraint parameters are analyzed. The simulation results show that the pitching attitude angles of the UUV and sub-vehicle in the separation process are negatively correlated with the change of elastic constraint stiffness, and the load is positively correlated with it, which are in opposite optimization directions. When the total stiffness of the elastic gaskets remains constant, changes in the number of elastic gaskets have a minimal impact on the UUV and sub-vehicle motion state during separation, but significantly affects the load fluctuations on the sub-vehicle, leading to structural vibration issues. The analysis method established in this paper is capable of quickly assessing the safety of underwater vehicle separation for different elastic gasket schemes, thereby facilitating the optimization of parameters. 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