Search Results (883)

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light markers with known geometry, and the follower determines its relative position based on data from an onboard camera without using a hydroacoustic communication channel or direct exchange of navigation information. To synthesize the control law, a reinforcement learning method based on the Proximal Policy Optimization algorithm is used. Policy learning is performed in a simulation environment, taking into account the dynamic model of the agent in the horizontal plane and observation noise. A structure of state space, actions, and reward function is proposed, aimed at minimizing the error in relative position and orientation. Additionally, Bayesian optimization of the weight coefficients of the reward function is performed. Bayesian optimization of the reward function weights reduces the RMS tracking error from 0.24 m to 0.09 m and demonstrates that heading regulation has a significantly stronger impact on stability than position penalties. The results of modeling, testing in the Webots environment, and experiments on MiddleAUV class devices confirm the feasibility and scalability of the approach. It is shown that a single trained policy ensures stable formation maintenance when the number of follower agents and initial conditions change without additional retraining. Full article

Autonomous obstacle avoidance is a critical capability for Autonomous Underwater Vehicles (AUVs) to operate safely in dynamic and uncertain marine environments. Traditional AUV control methods rely on precise physical modeling and preset rules, yet they struggle to adapt to multiple sources of uncertainty, such as random initial states, dynamic obstacles, and varying currents. In recent years, deep reinforcement learning has provided a new avenue for data-driven adaptive policy learning. However, it remains insufficient for handling long-horizon tasks with sparse rewards. While hierarchical reinforcement learning can mitigate reward sparsity through temporal abstraction, it often faces challenges including exploration–exploitation imbalance, slow global convergence, and insufficient safety guarantees. Furthermore, most existing studies neglect dynamic environmental disturbances and task continuity, which further limits the practical application of these algorithms. To address these challenges, this paper proposes a hierarchical curiosity-driven AUV obstacle avoidance algorithm (HDAO), designed for autonomous obstacle avoidance in dynamic and uncertain underwater environments. The core design of HDAO incorporates several key innovations. Firstly, it introduces a Collision Threat Index for dynamic obstacles, which enables explicit risk perception and quantifies collision threats, thereby enhancing the policy’s generalization and robustness. Secondly, a task-decoupled hierarchical architecture is employed to synergistically optimize global path planning and local obstacle avoidance behaviors. This approach effectively manages long-horizon navigation tasks while alleviating high-dimensional training pressure. Finally, a novel reward mechanism is designed by integrating hierarchical active exploration with curiosity-driven passive exploration. This mechanism effectively incentivizes the agent to explore unvisited areas under sparse reward conditions and dynamically balances exploration and exploitation. Experimental results demonstrate that HDAO significantly outperforms existing methods in terms of obstacle avoidance success rate, training convergence speed and robustness against external disturbances. Full article

For AUVs operating under large-rudder-angle yaw maneuvering conditions, linearized hydrodynamic-derivative models often fail to accurately capture strongly nonlinear flow effects, and the applicability of control parameters becomes limited. To address these issues, this paper proposes a CFD-in-the-loop control simulation and parameter optimization framework for large-rudder-angle yaw maneuvers. Based on a coupled hull–propeller–rudder solution method, an unsteady CFD motion simulation model is developed that simultaneously accounts for propeller wake, rudder inflow, and hull-flow interaction, thereby enabling a strongly coupled solution of flow-field evolution and the six-degree-of-freedom motion of the vehicle. On this basis, a CFD-in-the-loop closed-loop control simulation framework is established by integrating the controller, actuator dynamic model, virtual sensors, and CFD motion simulation module into a unified framework, thereby realizing closed-loop computation of control input, flow response, motion update, and state feedback. Furthermore, under the same controller structure and parameter settings, the large-rudder-angle yaw responses predicted by the linearized hydrodynamic-derivative model and the CFD-in-the-loop simulation framework are compared and analyzed. This comparison reveals the dependence of control parameters on the underlying dynamic model and highlights their limited applicability under strongly nonlinear operating conditions. Finally, to address the high computational cost of CFD-in-the-loop simulations, a surrogate-model-based control parameter optimization method is developed to improve parameter tuning efficiency and enhance closed-loop control performance. The results show that the proposed CFD-in-the-loop control simulation framework can effectively characterize the nonlinear hydrodynamic effects arising during large-rudder-angle maneuvers, and provides a more physically consistent basis for control parameter optimization, analysis, and design. Full article

This study presents a simulation-based evaluation of a wall-following and mapping framework for autonomous underwater vehicles (AUVs) equipped with a single-line laser, targeting structured environments such as rectangular tanks and dam interiors. A hardware-in-the-loop (HIL) simulation platform is developed to integrate sensor emulation, vehicle dynamics, and image-based control while preserving the onboard data formats, update rates, and communication protocols of the AUV system. Using a single camera–laser pair, the framework estimates yaw angle and lateral wall distance from laser image geometry to support real-time wall-following and frontal obstacle avoidance. Wall mapping is performed by transforming laser image features into spatial coordinates and estimating the dimensions of geometric protrusions. The framework is evaluated on simulated walls with protruding features under two navigation conditions: ideal-motion and dynamic-control operation. Simulation results show stable wall-following performance, with lateral distance errors typically below 0.1 m. Under ideal-motion conditions, mapping errors range from 1% to 13%, while under dynamic-control navigation they increase to 10–35% due to attitude fluctuations and control-induced motion. Frontal obstacle avoidance maintains a minimum clearance of 1.04 m. The results demonstrate the feasibility of using a single-line laser and a unified image stream for both real-time wall-following control and post-mission geometric mapping within the defined simulation conditions. While the evaluation is limited to simulation and assumes idealized optical conditions without modeling hydrodynamic disturbances or optical degradation effects, the framework provides a system-level reference for laser-guided inspection strategies in confined underwater environments such as tanks, reservoirs, and dams. Full article

Autonomous Underwater Vehicles (AUVs) play a crucial role in deep-sea exploration missions. In the complex and highly dynamic marine environment, it is essential for AUVs to possess robust fault diagnosis capabilities to enhance their operational safety. In the context of AUV fault diagnosis, closed-set recognition methods tend to misclassify unknown faults as known ones, which may lead to severe operational consequences. In order to enable AUVs to adapt to new and unknown deep-sea environments and effectively detect new unknown faults, this paper proposes an open-set AUV fault recognition method based on a Convolutional Neural Network (CNN). Firstly, the CNN is employed to extract high-level discriminative features from raw sensor data. Then, a committee consisting of multiple one-class SVMs (OC-SVMs) is constructed to determine whether the input sample belongs to a known category. Finally, the identified known samples are accurately classified via the designed classifier module. This method can effectively distinguish between known faults and unknown faults. To improve the recognition accuracy of the model, an attention mechanism is introduced. By learning to automatically assign weights to different feature channels, the model can focus on more important or relevant feature channels. Experiments based on the “Haizhe” dataset demonstrate that the proposed CNN-OC-SVM model exhibits superior performance in AUV fault diagnosis tasks compared with the state-of-the-art and traditional methods. Full article

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments. Full article

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves $\mathrm{SNR}$ by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration. Full article

The growing demand for offshore oil and gas production in deep waters has motivated the development of technologies to enable the continuous, reliable, and cost-effective monitoring of subsea equipment. Traditional inspection techniques rely on ROVs and AUVs, leading to delays between data acquisition and recovery and high operational costs. Underwater acoustic communication systems represent an attractive alternative for transmitting monitoring data to the surface in real time. This work evaluates the feasibility of implementing an underwater acoustic communication link for data transmission in deep-water environments, considering environmental conditions and acoustic channel characteristics. Using the BELLHOP ray-tracing model, simulations were performed to predict transmission loss, multipath effects, ambient noise, and the resulting signal-to-noise ratio (SNR) for different modem configurations and operating frequencies. The results demonstrate that the performance of the underwater link is strongly dependent on frequency, distance, and environmental variability. The study identifies optimal frequency–range relationships, quantifies the limitations imposed by transmission loss and ambient noise, and provides guidance for selecting acoustic modem parameters for real subsea monitoring applications. The SNR for three modem models operating at different frequencies illustrates the signal detection capability in the marine environment. The differences between modems A, B, and C are defined by their technical specifications and how they perform within the underwater acoustic channel of the Campos Basin. The data transmission capacity is supported by the data rates provided by the analyzed modems. The low frequencies of modem A (9.75 kHz) achieve the highest SNR, enabling long-range monitoring. At higher frequencies, modem C (78 kHz) allows short-distance communication. Modem B (35 kHz) offers a good balance between the data rate and power consumption, consuming only 1 W, making it highly viable for monitoring systems that rely on batteries and require long-term operation. The findings support the feasibility of integrating underwater acoustic communication into subsea monitoring architectures, enabling a more efficient oversight of deep-water production systems. The analysis concludes that project viability depends on selecting a system where the SNR and range meet the specific monitoring requirements. Full article

Model-aided navigation is a key approach for enhancing the positioning accuracy of autonomous underwater vehicles (AUVs). However, its precision is often degraded by model-based velocity errors arising from attitude-induced deviations and uncertainties in the mapping between propeller rotational speed and vehicle velocity. To overcome these limitations, this study proposes an attitude-compensated and acoustics-calibrated model-aided navigation framework for AUVs. The framework derives the vertical velocity from pressure sensor depth data to correct attitude-related model errors. It also dynamically refines the mapping between propeller speed and velocity using long-baseline (LBL) acoustic positioning data when LBL measurements are available. A sea trial was conducted in the South China Sea at a depth of 2000 m to verify the proposed method. The results showed that the system maintained a positional accuracy of 509 m over 5 h beyond LBL coverage. This outcome demonstrates its ability to achieve sustained high-precision navigation without external assistance. Full article

The rapid development of Autonomous Underwater Vehicles (AUVs) has increased the demand for propulsion systems that balance thrust density, hydrodynamic efficiency, and acoustic discretion. This study presents a comprehensive numerical investigation of the performance of the Blue Robotics T500 thruster, embedded within the RAS-HA-X25 AUV’s internal conduit. Using transient Computational Fluid Dynamics (CFD) within the OpenFOAM framework, this research assesses the propulsive characteristics of the thruster across six distinct outlet geometries, including convergent jet nozzles and multi-lobed “daisy” configurations. To improve computational efficiency for parametric design, a calibrated actuator disc model was developed and validated against resolved-rotor simulations, revealing a 15% discrepancy attributed to tip leakage and hub vortex effects. Results show that at the operational advance ratio ($J=0.167$), the 60 mm convergent nozzle is the optimal configuration for maximising thrust, achieving a peak net thrust of 42 N. In contrast, the daisy-type lobed geometries, while causing a 50% reduction in absolute thrust compared to a standard cylindrical pipe, significantly homogenise the exit-plane velocity distribution and reduce swirl intensity. These findings indicate that lobed terminations provide a viable mechanism for reducing hydroacoustic signatures, offering a strategic “stealth” advantage for low-observable underwater platforms where acoustic discretion is prioritised over pure thrust density. This study establishes a robust methodology for optimising embedded propulsion modules in next-generation autonomous and hybrid underwater vehicles. Full article

This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on the kinematic and dynamic models of autonomous underwater vehicle, virtual velocity commands are constructed via backstepping approach to stabilize the position and attitude errors. To circumvent the “differential explosion” problem inherent in conventional backstepping control caused by repeated differentiations of virtual control variables, first-order low-pass filters are introduced to construct dynamic surface control, yielding smooth derivatives of virtual velocity commands. Secondly, to enhance convergence rate and robustness, a non-singular terminal sliding surface is designed at the dynamic level, and a terminal reaching law is formulated to achieve finite-time convergence of velocity tracking errors. Furthermore, to compensate for external disturbances and unmodeled dynamics, a disturbance observer based on the super-twisting algorithm is developed, enabling finite-time high-precision estimation of lumped disturbances, with the estimation results incorporated into the control law for feedforward compensation. Finally, comparative simulations are conducted under two typical disturbance scenarios. The results demonstrate that the proposed method achieves instantaneous disturbance estimation (reducing convergence time from 3 s to near zero), significantly smoother control inputs, and superior tracking accuracy with RMSE as low as 0.6788 m and MAE as low as 0.1468 m, reducing errors by up to 30.6% compared to baseline methods. Full article

Due to challenges such as inadequate lighting, water scattering, high density of small objects, and complex object morphology in underwater environments, traditional YOLO11 models face difficulties including interference from complex backgrounds, weak perception of small objects, and insufficient feature extraction when applied underwater. This paper proposes an improved MIP-YOLO11 model for underwater object detection based on the YOLO11 framework. First, a MCEA module is designed in the backbone network to replace the basic CBS convolution module. Through a lightweight multi-branch convolutional structure, the perception ability for small objects, object edges, contours, and morphological features in underwater scenes are enhanced without significantly increasing computational overhead. Second, an IMCA module based on the coordinate attention mechanism is introduced at the end of the backbone network to replace the C2PSA module, reducing the number of model parameters while maintaining detection accuracy. Finally, the Bottleneck module in C3k2 is improved by incorporating a PConv and a dual residual connection mechanism, thereby expanding the receptive field and enhancing the efficiency of complex feature extraction. Experimental results demonstrate that MIP-YOLO11 significantly outperforms the traditional YOLO11 in underwater environments. P and R are improved by 2.5% and 4.1%, respectively. Moreover, the mAP0.5 and mAP0.5:0.95 metrics are increased by 4.2% and 7.5%, respectively. The improved model achieves a good balance between high accuracy and light weight, and can provide a more reliable underwater object detection scheme for AUV underwater detection and other application scenarios. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article