Search Results (233)

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model’s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model’s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model’s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model’s performance and fulfilling the real-time detection criteria for underwater robots. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

Hydrodynamic coefficients determine the behavior of all simulated underwater vehicles. Therefore, it is essential to precisely define their values when aiming to replicate a real vehicle. Generally established procedures for obtaining them tend to have limitations, especially in transient responses. To address these issues, this paper proposes a comprehensive methodology for obtaining the hydrodynamic coefficients of an underwater vehicle. The main novelty is the combination of empirical measurements as a first step and evolutionary algorithms as a final step for optimizing the coefficients. The proposed methodology is described and applied to a commercially available remotely operated vehicle (ROV) BlueROV2, followed by analyzing the results in detail and including several tests that compare it to the real vehicle to validate its adequacy. Full article

A previously buried paleosol was found on the continental shelf during a study of sea floor scour, nucleated by large artificial reef structures such as vessel hulks, barges, train cars, military vehicles, etc., called “scour nuclei”. It is a relic paleo-land surface of sapling-sized tree stumps, root systems, and fossil animal bone exhumed by scour processes active adjacent to the artificial reef structure. Over the span of five research cruises to the site in 2022–2024, soil samples were taken using hand excavation, PONAR grab samplers, split spoon, hollow tube auger, and a modified Shelby-style push box. High-definition (HD) video was taken using a Remotely Operated Vehicle (ROV) and diver-held cameras. Radiocarbon dating of wood samples returned ages of 42,015–43,417 calibrated years before present (cal yrBP). Pollen studies, together with the recovered macrobotanical remains, support our interpretation of the site as a freshwater forested wetland whose keystone tree species was Taxodium distichum—bald cypress. The paleosol was identified as an Aquult, a sub-order of Ultisols where water tables are at or near the surface year-round. A deep (0.25 m+) argillic horizon comprised the bulk of the preserved soil. Comparable Ultisols found in Georgia wetlands include Typic Paleaquult (Grady and Bayboro series) soils. Full article

Remotely operated vehicles (ROVs) face challenges in maneuvering and rapidly detecting and repairing large offshore platforms. The accurate research on the hydrodynamics of the ROV, which moves close to the wall, is of great significance for its maneuverability. This study uses computational fluid dynamics (CFDs) to analyze the hydrodynamic characteristics of an ROV when it is moving near the wall, considering factors such as structural asymmetry, speed, and distance from the wall. This study applies multiple linear regression to extract relevant hydrodynamic coefficients and develops a mathematical model that simulates the impact of these factors on ROV performance. The results indicate that the wall’s influence on hydrodynamic forces is significant. Total resistance increases as the ROV moves closer to the wall, and the effect becomes more pronounced at higher speeds. Pressure differential resistance is the dominant factor affecting ROV performance, while viscous resistance remains low and is mostly unaffected by wall proximity. These findings provide valuable insights into calculating hydrodynamic coefficients and modeling the dynamics of ROVs with complex shapes operating near the wall. Full article

This study focuses on the remotely operated underwater vehicle (ROV) and addresses key issues in existing simulation systems, such as neglecting the influence of ocean currents on the ROV’s trajectory or only simulating the impact of ocean currents instead of combining wake flow and ocean currents. Additionally, the visualization capabilities of current simulation systems still have room for improvement. This paper develops a three-dimensional path simulation system for ocean inspection robots to tackle these challenges based on MATLAB and Simulink. The system optimizes the drag matrix of the original simulation model by decomposing the sea current into three directional components in three-dimensional space and simulating the relative velocity in each direction separately; it introduces the influence of the current wake, thus more accurately realizing the trajectory simulation of the ROV under the current perturbation. Experimental results demonstrate high consistency between the optimized model’s simulation outcomes and theoretical expectations. The proposed system significantly improves trajectory evolution stability and consistency, compared to traditional models. The findings of this study indicate that the proposed optimized simulation system not only effectively verifies the applicability of control algorithms but also provides reliable data support for ROV design and optimization. Additionally, it lays a solid foundation for further developing intelligent underwater robots based on Internet of Things (IoT) technology. Full article

Marine litter threatens ocean ecosystems, and nautical tourism, as a source of litter, contributes significantly. This paper presents a qualitative and quantitative study of seafloor litter in the Bay of Selehovica in the northern Adriatic Sea. The bay is accessible only by sea and is attractive to nautical tourism vessels. The survey was conducted using a remotely operated vehicle across 22,100 m² of seafloor, before and after the tourist season (summer) in 2024. The analysis shows a 25.90% increase in litter items after one season. The predominant litter category is plastic, followed by glass, metal, rubber, and textiles. The abundance of marine litter increased from 1.3 to 1.7 items per 100 m² in the post-season, reflecting a measurable rise in litter density. Due to non-normal data distribution (Shapiro–Wilk test, p < 0.001), the Wilcoxon Signed-Rank Test was used, revealing a statistically significant increase in marine litter (W = 0, p < 0.001) with a large effect size (Cohen’s d = 0.89). A strong positive correlation between the pre- and post-season values was observed (Spearman’s r = 0.96, p < 0.001), suggesting that areas with higher initial litter levels tend to accumulate more over time. The results point to the necessity of targeted management strategies to reduce the pressure of nautical tourism on marine ecosystems and to protect the marine environment. Full article

Traditional track-driven deep-sea nodule mining solutions significantly disrupt seabed ecosystems, making them unsuitable for commercial application. In contrast, ROV-like alternatives, such as the hovering mining vehicle, or HMV, offer substantial improvement in this regard and are deemed to be a viable way forward. This paper proposes an adaptive neural network fuzzy sliding mode controller architecture for the underwater trajectory tracking of HMV. The algorithm, named the Adaptive Radial Basis Function Neural Network Fuzzy Sliding Mode Controller (ARFSMC), replaces modeled vehicle dynamics with a radial basis function neural network (RBFNN). To enhance disturbance rejection, an adaptive mechanism is applied to the RBFNN output weighting matrix. Additionally, a fuzzy inference system (FIS) is implemented as the switching term, replacing the traditional signum function, to reduce high-frequency oscillations in the control signal. The stability of the algorithm under unknown external disturbance was confirmed via Lyapunov stability analysis. To validate the ARFSMC’s performance, three numerical simulation cases were conducted, each designed to reflect an expected operation scenario of the HMV, through which the tracking performance of the ARFSMC under time-varying system inertia is validated and benchmarked against conventional sliding mode control (CSMC) and double-loop sliding mode control (DSMC). The simulation results confirm that comparing the above two controllers, the root mean square error (RMSE) of the ARFSMC is reduced by 15.0% and 11.4%, respectively. And when comparing the CSMC, the chattering is reduced by 97.8%. Both indicate their high robustness and superior performance in tracking control. The controller development and numerical validation in this work are aimed at the trajectory tracking challenge of the HMV in deep-sea mining operation. The dynamical modeling of the vehicle is based on parameters of the HaiMa ROV. External disturbance from currents were considered as sinusoidal functions modified with random noise. Full article

This paper presents a predefined-time control approach to address slow convergence and instability in the orbit control of remotely operated vehicles (ROVs). The proposed method introduces tunable predefined-time stability (PTS), allowing precise adjustment of the system’s stability time through configurable parameters, thereby enhancing controller adaptability. A control input system ensures PTS is developed, while a fuzzy logic system (FLS) is employed to estimate unstructured uncertainties and disturbances. This integration improves robustness, reduces chattering, and eliminates singularities, making the approach well suited for systems with incomplete or unknown model data. Comprehensive simulations validate the effectiveness of the proposed method, demonstrating superior performance compared to existing control strategies and highlighting its potential for advanced ROV applications. Full article

Background and objectives: Acute gastroenteritis (AGE) remains a significant cause of morbidity in children, particularly in low- and middle-income countries. Viral pathogens, including rotavirus (RoV), norovirus (NoV), and adenovirus (HAdV), are among the leading causes of AGE. This study aimed to determine the prevalence of viral, bacterial, and parasitic enteric pathogens associated with AGE among hospitalized children in Northern Jordan. Materials and Methods: A total of 195 stool samples were collected from hospitalized children with AGE during the winter seasons of 2022–2024. Multiplex real-time qPCR assays were performed to detect common pathogens. The prevalence of each pathogen was determined, and co-infections were analyzed. Clinical symptoms, demographic characteristics, and associations between specific pathogens and disease severity were evaluated. Results: Viral pathogens were the predominant cause of AGE, with NoV detected in 53 cases (27.2%; of which 19.0% were NoV GI and 8.2% NoV GII), followed by RoV (24.1%), HAdV (20.0%), HAstV (13.3%), and SaV (12.3%). Co-infections were observed in several cases, particularly among viral infections evoked by RoV, HAdV, and NoV GI. Bacterial and parasitic infections were less prevalent, with Salmonella and Campylobacter spp. detected in 23.1% and 13.8%, respectively. Additionally, Cryptosporidium was identified in two cases (0.5%). Conclusions: Viral pathogens, particularly NoV, RoV, and HAdV, are the leading causes of AGE among hospitalized children in Jordan. Co-infections among viral pathogens were common, whereas bacterial and parasitic infections played a limited role in the disease burden. These findings emphasize the importance of continued surveillance and vaccination efforts, particularly for RoV, to reduce AGE-related hospitalizations in children. Full article

This study evaluates the economic viability of the South Pasopati Coal Project in Indonesia, addressing market volatility, carbon pricing policies, and the country’s energy transition towards Net Zero Emissions (NZE). Given Indonesia’s reliance on coal and the increasing global shift toward renewable energy, traditional valuation methods such as Discounted Cash Flow (DCF) may not adequately capture uncertainty and strategic flexibility. The study applies Real Options Valuation (ROV), integrating Monte Carlo Simulation (MCS) and Binomial Lattice Modeling, to assess project feasibility under various scenarios. The research compares three valuation scenarios: the base scenario (eastern route), an alternative scenario (western route), and a carbon pricing scenario. Results indicate that while the DCF method estimates a positive Net Present Value (NPV) for the base scenario, it fails to incorporate price volatility risks. The ROV method, however, captures managerial flexibility and provides a more robust valuation, showing an Expanded NPV (ENPV) that better reflects market uncertainties. Findings suggest that implementing ROV improves decision-making, particularly in volatile markets. The study underscores the necessity for Indonesia to adopt more flexible valuation frameworks to enhance investment decisions in the coal sector while aligning with international environmental standards. Full article

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy. Full article

In order to realize the detection of marine engineering facilities, the hardware system of a new type of remotely operated vehicle (ROV) is designed independently, and the control system, including the lower computer program and the upper computer software, is developed. At the same time, in order to explore the thrust distribution of the thruster and realize the optimization of the thrust distribution under the installation position and installation angle of the designed thruster, the mathematical model of the ROV propulsion system is established. The simulation models of ROV motion control and thrust distribution are established in MATLAB R2022a and Unity 3D, respectively. Given the thrust input of the compound motion, the sequential quadratic programming (SQP) method and the direct logic method are used to compare the simulation results of thrust distribution. Finally, the underwater attitude control experiment and the application experiment of the actual scene are carried out. Combined with the simulation and experimental results, the feasibility of using the sequential quadratic programming method to optimize the thrust allocation is verified, and it is shown that the new ROV system can basically meet the expected design requirements. Full article

Marine mammal monitoring, a growing field of research, is critical to cetacean conservation. Traditional ‘tagging’ attaches sensors such as GPS to such animals, though these are intrusive and susceptible to infection and, ultimately, death. A less intrusive approach exploits UUV commanded by a human operator above ground. The development of AI for autonomous underwater vehicle navigation models training environments in simulation, providing visual and physical fidelity suitable for sim-to-real transfer. Previous solutions, including UVMS and L2D, provide only satisfactory results, due to poor environment generalisation while sensors including sonar create environmental disturbances. Though rich in features, image data suffer from high dimensionality, providing a state space too great for many machine learning tasks. Underwater environments, susceptible to image noise, further complicate this issue. We propose SWiMM_2.0, coupling a Unity simulation modelling of a BLUEROV UUV with a DRL backend. A pre-processing step exploits a state-of-the-art CMVAE, reducing dimensionality while minimising data loss. Sim-to-real generalisation is validated by prior research. Custom behaviour metrics, unbiased to the naked eye and unprecedented in current ROV simulators, link our objectives ensuring successful ROV behaviour while tracking targets. Our experiments show that SAC maximises the former, achieving near-perfect behaviour while exploiting image data alone. Full article