Search Results (793)

Historically, the Mediterranean Sea has been an area of cultural exchange and maritime commerce. One out of many submerged archaeological sites is the Roman shipwreck that was discovered in 2006 off the coast of Santo Stefano al Mare, in the Ligurian Sea, Italy. The wreck was dated to the 1st century B.C. and consists of a well-preserved cargo ship of Roman amphorae that were likely used for transporting wine. In this study, we present the results of the first underwater survey of the wreck using an Autonomous Underwater Vehicle (AUV) industrialized by Graal Tech. The AUV was equipped with a NORBIT WBMS multibeam sonar, a 450 kHz side-scan sonar, and inertial navigation systems. The AUV conducted multiple high-resolution surveys on the wreck site and the collected data were processed using geospatial analysis methods to highlight local anomalies directly related to the presence of the Roman shipwreck. The main feature was an accumulation of amphorae, covering an area of approximately 10 × 7 m with a maximum height of 1 m above the seabed. The results of this interdisciplinary work demonstrated the effectiveness of integrating AUV technologies with spatial analysis techniques for underwater archaeological applications. Furthermore, the success of this mission highlighted the potential for broader applications of AUVs in the study of the seafloor, such as monitoring seabed movements related to offshore underground energy storage or the identification of objects lying on the seabed, such as cables or pipelines. Full article

The submarine topography in the canyon area of the Qiongdongnan Basin is complex, with severe risks of shallow gas hazards threatening marine engineering safety. To accurately characterize seabed morphology and assess shallow gas risks, this study employed multi-source data fusion technology, integrating 3D seismic data, shipborne multibeam bathymetry data, and high-precision AUV topographic data from key areas to construct a refined seabed terrain inversion model. For the first time, the spatial distribution characteristics of complex geomorphological features such as scarps, mounds, fissures, faults, and mass transport deposits (MTDs) were systematically delineated. Based on attribute analysis of 3D seismic data and geostatistical methods, the enrichment intensity of shallow gas was quantified, its distribution patterns were systematically identified, and risk level evaluations were conducted. The results indicate: (1) multi-source data fusion significantly improved the resolution and accuracy of terrain inversion, revealing intricate geomorphological details in deep-water regions; and (2) seismic attribute analysis effectively delineated shallow gas enrichment zones, clarifying their spatial distribution patterns and risk levels. This study provides critical technical support for deep-water drilling platform site selection, submarine pipeline route optimization, and engineering geohazard prevention, offering significant practical implications for ensuring the safety of deep-water energy development in the South China Sea. Full article

High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model–data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a line-of-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection. Full article

This paper addresses the trajectory tracking control problem of Autonomous Underwater Vehicles (AUVs). A comprehensive mathematical model is first established based on Newtonian mechanics, incorporating both kinematic and dynamic equations. By reasonably neglecting the minor influence of roll motion, a five-degree-of-freedom (5-DOF) underactuated AUV model is derived. Considering the strong nonlinearities, high coupling, and time-varying hydrodynamic parameters typical of underwater environments, a fuzzy adaptive PID controller is proposed. This controller combines the adaptability of fuzzy logic with the structural simplicity and reliability of PID control, making it well-suited to the demanding requirements of AUV motion control. Extensive simulation experiments are conducted to evaluate the controller’s performance under various operating conditions. The results show that the fuzzy adaptive PID controller significantly outperforms conventional PID and standalone fuzzy logic controllers in terms of convergence speed and oscillation suppression. Furthermore, a theoretical stability analysis is provided to ensure that the proposed control system remains stable under time-varying fuzzy gain scheduling, confirming its effectiveness and potential for practical application in underwater vehicle control. Full article

The endurance of autonomous underwater vehicles (AUVs) has long been constrained by limited energy replenishment. Underwater inductive wireless power transfer (UIWPT), with its contactless power transfer capability, offers an innovative solution for efficient underwater charging of AUVs. This paper provides a systematic review of the architecture of UIWPT systems, analyzes key power loss mechanisms and corresponding optimization strategies, and summarizes the latest research progress in magnetic coupler design, compensation circuit topologies, control methods, simultaneous power and data transfer, and seawater-induced eddy current losses. Representative cases of UIWPT system integration on AUV platforms are also reviewed, with particular emphasis on environmental factors such as salinity variation, biofouling, and deep-sea pressure, as well as EMC, which critically constrain engineering applications. Finally, this paper discusses development trends including high-efficiency power transfer, enhanced reliability under extreme environments, and practical deployment challenges, and it presents a forward-looking technical roadmap towards long-term, autonomous, and intelligent underwater wireless power transfer. Full article

This paper addresses the spatial path tracking problem of the X-rudder autonomous underwater vehicle (AUV) under random sea current disturbances. An adaptive line-of-sight guidance-linear quadratic regulator (ALOS-LQR) control strategy with roll constraints is proposed to enhance the tracking control accuracy and stability of the X-rudder AUV in such environments. First, to mitigate the roll-instability-induced depth and heading coupling deviations caused by unknown environmental disturbances, a roll-constrained linear quadratic regulator (LQR) heading-pitch control strategy is designed. Second, to handle random disturbances and model uncertainties, a nonlinear extended state observer (ESO) is employed to estimate dynamic disturbances. At the kinematic level, an adaptive line-of-sight guidance method (ALOS) is utilized to transform the path tracking problem into a heading and pitch tracking problem, while compensating in real time for kinematic deviations caused by time-varying sea currents. Finally, the effectiveness of the proposed control scheme is validated through simulation experiments and lake trials. The results confirm the effectiveness of the proposed method. Specifically, the roll-constrained ESO-LQR reduces lateral and longitudinal errors by 77.73% and 80.61%, respectively, compared to the roll-constrained LQR. ALOS navigation reduced lateral and longitudinal errors by 85.89% and 94.87%, respectively, compared to LOS control, while exhibiting faster convergence than ILOS. In physical experiences, roll control reduced roll angle by 50.52% and depth error by 33.3%. Results demonstrate that the proposed control strategy significantly improves the control accuracy and interference resistance of the X-rudder AUV, exhibiting excellent accuracy and stability. Full article

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article

The global demand for innovative underwater applications is increasing, encompassing scientific research, commercial endeavors, and defense operations. A significant challenge these applications face is fulfilling the energy requirements of underwater devices. This challenge extends beyond powering individual devices to include the entire network of underwater robotic sensors. These devices have varying energy needs; some are mobile while others are stationary, and they operate under diverse environmental conditions, such as different depths, temperatures, pressures, currents, and salinity levels. This paper compares the latest state-of-the-art research on powering underwater devices, addressing the challenges and practical considerations. It examines two primary approaches: first, energy harvesting from the natural environment, and second, the use of wireless power transfer (WPT). While energy harvesting methods have been established, their effectiveness greatly depends on the specific environment in which they are deployed, making them less viable as a universal solution. On the other hand, WPT presents its challenges, particularly as its efficiency diminishes with distance. Nonetheless, it remains a promising option, and further research is essential to explore its potential, including the integration of other technologies to develop hybrid solutions that leverage multiple power sources. Full article

This paper proposes a methodology for the generation and registration of three-dimensional data sets to support an adaption of Teach and Repeat path following for an Autonomous Underwater Vehicle (AUV) equipped with a multibeam sonar system. The goal of this system is to enable an AUV to generate a topological map of a path consisting of locally consistent sub maps and to re-follow this path using newly collected data. For AUVs traversing long distances without external navigational aids, this methodology would allow robust return-to-home capability, specifically in remote and harsh environments such as beneath ice. Full article

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk of failure due to unpredictable maritime conditions. Considering the limitations in communication during the mission, docking failure can lead to the loss of collected data and failure of the entire AUV mission. In this study, a hypothetical AUV docking scenario was defined based on expert knowledge and without actual operational data. A Markov chain-based probabilistic model was employed to quantitatively assess the risk of the system during the mission. Environmental factors were excluded from the evaluation, and the simulation results were classified into five categories: success, timeout, internal component failure, exceeding a predefined sequence repetition limit, and spending the electrical energy under the battery SOC threshold. By analyzing the failure points of each category, strategies to improve the scenario success rate were discussed. This study quantitatively identified the interactions between constraints and risk factors that should be considered when establishing AUV docking plans through a virtual scenario-based failure analysis, thereby providing an evaluation framework that can be utilized in actual design. Full article

This paper proposes an optimal-sliding-mode-based adaptive dynamic programming (ADP) master–slave synchronous control strategy for the actuator saturation and performance constraints that AUVs face in dynamic recovery. First, by introducing the sliding-mode function into the value function to optimize the state error and its derivative simultaneously, the convergence speed is significantly improved. Second, by designing the performance constraint function to directly map the sliding-mode function, the evolution trajectory of the sliding-mode function is constrained, ensuring the steady-state and transient characteristics. In addition, the hyperbolic tangent function (tanh) is introduced into the value function to project the control inputs into an unconstrained policy domain, thereby eliminating the phase lag inherent in conventional saturation compensation schemes. Finally, the requirement for initial stability is relaxed by constructing a single-critic network to approximate the optimal control policy. The simulation results show that the proposed method has significant advantages in terms of the position and attitude synchronization error convergence rate, steady-state accuracy, and control signal continuity compared with the conventional ADP method. Full article

This paper addresses the three-dimensional trajectory tracking control problem for the underactuated Autonomous Underwater Vehicle (AUV) operating in complex ocean environments characterized by dynamic disturbances and model uncertainties. A super-twisting extended state observer (STESO) was designed to accurately estimate and compensate for external disturbances and unmodeled dynamics in finite time. A fractional-order proportional–integral–derivative (FOPID) controller was then developed based on the disturbance estimates provided by the STESO. Leveraging the superior frequency-domain tuning flexibility of fractional calculus, the controller enhances tracking precision and robustness against dynamic disturbances. Furthermore, a strict Lyapunov-based stability analysis is presented, and the tracking error converges to zero asymptotically when disturbance estimation errors vanish. Numerical simulations validated the effectiveness and robustness of the proposed control strategy under various disturbance scenarios. Full article

Autonomous underwater vehicles (AUVs) play a pivotal role in the exploration and monitoring of the sea floor. A primary challenge in surveying AUVs is consistently obtaining high-quality optical imagery data. A major cause of quality reduction is turbid water, which both attenuates and scatters light. The effects of turbidity can be minimized by lowering the operational altitude of the AUV, at the cost of increased survey duration and cost. Consequently, before conducting a survey, a trade-off must be made between the risk of acquiring suboptimal images and the additional time required to cover an area. In this research, we develop a computer-vision-based technique and control system that dynamically adjusts the altitude of an AUV based on real-time estimates of turbidity from collected images. Our testing in a simulated environment demonstrates that this system reliably improves the efficiency and quality of image collection. Full article

Aiming at the underwater docking process of an autonomous underwater vehicle (AUV) and conical docking bay, this paper systematically analyzes the influence of docking geometry parameters and ocean current conditions on hydrodynamic characteristics. By establishing a three-dimensional mathematical model and using computational fluid dynamics (CFD) methods, this study focused on investigating the effects of the taper angle and diameter of the docking chamber inlet, as well as the magnitude and direction angle of ocean currents, on the docking resistance, lift, velocity field, and pressure field distribution of an AUV. The results show the following: increasing the dock inlet taper can reduce the AUV docking drag; the dock inlet diameter is positively correlated with the AUV docking drag; the larger the current speed is, the more drastic the change of the AUV drag is; the larger the current direction angle is, the larger the velocity difference between the upper and lower flow fields of the AUV is, leading to a significant lift effect. The research results provide a theoretical basis for the structure optimization and control strategy design of AUV underwater docking systems. Full article

Autonomous Underwater Vehicles (AUVs) equipped with vision systems face unique challenges in real-time environmental perception due to harsh underwater conditions and computational constraints. This paper presents a novel cloud–edge framework for real-time vision–language analysis in underwater drones using the Qwen2.5-VL model. Our system employs a uniform frame sampling mechanism that balances temporal resolution with processing capabilities, achieving near real-time analysis at 1 fps from 23 fps input streams. We construct a comprehensive data flow model encompassing image enhancement, communication latency, cloud-side inference, and semantic result return, which is supported by a theoretical latency framework and sustainable processing rate analysis. Simulation-based experimental results across three challenging underwater scenarios—pipeline inspection, coral reef monitoring, and wreck investigation—demonstrate consistent scene comprehension with end-to-end latencies near 1 s. The Qwen2.5-VL model successfully generates natural language summaries capturing spatial structure, biological content, and habitat conditions, even under turbidity and occlusion. Our results show that vision–language models (VLMs) can provide rich semantic understanding of underwater scenes despite challenging conditions, enabling AUVs to perform complex monitoring tasks with natural language scene descriptions. This work contributes to advancing AI-powered perception systems for the growing autonomous underwater drone market, supporting applications in environmental monitoring, offshore infrastructure inspection, and marine ecosystem assessment. Full article