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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.

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV’s flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions.

Current methods for edge contour feature extraction for Unmanned Underwater Vehicle (UUV) side-scan sonar images have yet to solve the problems of low accuracy, discontinuous edges, and loss of detail. This paper proposes a new feature extraction method for UUV side-scan sonar images. By adding a batch normalization layer, the skip structure of the fully convolutional network (FCN) is improved, the redistribution of parameters in the skip structure is realized, and the training of the network is more sufficient. And we design a positive sample weighted loss function (WPSL) to improve the problem that the performance of the classification algorithm is degraded due to the imbalance of sample distribution in the data set. In this paper, an initial dataset is expanded by turning, rotating, and adding noise. An improved feature extraction network is then constructed, and the training of the improved FCN is completed by using a mini-batch gradient descent method, thus realizing accurate extraction of edge contour features of seabed topography. The experimental results show that the proposed method is more suitable to reject speckle noise than the traditional Canny and Fuzzy C-Means algorithms. Compared with current deep learning methods, the proposed method improves the ability to fuse detailed information and make discontinuous edges continuous. The mean intersection over union (IU) reaches 83.05%, which is 5.48% higher than the 77.57% before improvement.

This work addresses the motion control problem for a 4-degree-of-freedom unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. A disturbance observer-based control scheme is proposed, which is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the developed EAOB has the ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms an effective adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and robot operating system environment. The results show that the proposed method can effectively reject unpredictable disturbances and improve the UUV’s control performance.

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.

In many aquaculture farms, Unmanned Underwater Vehicles (UUVs) are being deployed to perform dangerous and time-consuming repetitive tasks (e.g., fish net-pen visual inspection) on behalf of or in collaboration with farm operators. Mostly, they are remotely operated, and one of the main barriers to deploying them autonomously is the UUV localisation. Specifically, the cost of the localisation sensor suite, sensor reliability in constrained operational workspace and return on investment (ROI) for the huge initial investment on the UUV and its localisation hinder the R&D work and adoption of the autonomous UUV deployment on an industrial scale. The proposed system, which leverages the AprilTag (a fiducial marker used as a frame of reference) detection, provides cost-effective UUV localisation for the initial trials of autonomous UUV deployment, requiring only minor modifications to the aquaculture infrastructure. With such a cost-effective approach, UUV R&D engineers can demonstrate and validate the advantages and challenges of autonomous UUV deployment to farm operators, policymakers, and governing authorities to make informed decision-making for the future large-scale adoption of autonomous UUVs in aquaculture. Initial validation of the proposed cost-effective localisation system indicates that centimetre-level accuracy can be achieved with a single monocular camera and only 10 AprilTags, without requiring physical measurements, in a 115.46 m3 laboratory workspace under various lighting conditions.

Unmanned underwater vehicle (UUV) is a key technology for marine resource exploration and ecological monitoring. How to use vision-based active positioning and three-dimensional perception to realize UUV underwater autonomous navigation and positioning is the basis for UUV's underwater operations. The complexity and unstructured characteristics of seawater bring new challenges to vision-based underwater high-precision positioning. Traditional visual localization algorithms mainly include geometric-based visual localization algorithms (such as ORB-SLAM2) and deep learning-based visual localization algorithms (such as DXSLAM). In this paper, based on the typical marine environment (low brightness, dynamic fish interference, underwater light spot, high turbidity), the experimental analysis and comparison of different visual positioning methods of UUV is carried out, which provides a reference for realizing the real-time localization of UUV, and further provides a better solution for UUV underwater measurement and monitoring operations.

With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution.

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.

This paper investigates the stability issues of the Unmanned Surface Vehicle (USV)–Unmanned Underwater Vehicle (ROV) system induced by cable loads under real marine conditions and high-speed operation. This study focuses on the dynamic coupling characteristics of cable forces affecting the unmanned platform and outlines the variation patterns of these forces under different operational scenarios. By developing the dynamic models of the USV, UC, and UUV, a comprehensive system model for the unmanned marine platform is constructed. The accuracy of the cable model is validated through experimental results, and the coupling interference effects of the cable during collaborative operations are systematically analyzed from multiple perspectives. Additionally, the cable tension and force behaviors under high-speed cruising conditions are thoroughly examined. The results provide a solid foundation for the development of cable load prediction models for collaborative marine unmanned platforms, and offer both theoretical and numerical insights for dynamic control strategies based on cable force adjustments.