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The evolution of submarine canyons is primarily controlled by turbidity currents, which erode and fill them over time; however, many other hydrodynamic currents operate within canyons. Bottom currents from these other hydrodynamic processes, including internal tides, can be dominant processes, but their deposits are seldom recognized in sediment cores or the rock record. This study combines autonomous underwater vehicle swath bathymetry imagery and sub‐bottom profiles, high‐resolution sediment core analyses (X‐ray imagery and thin sections), and previously collected seabed video and flow measurements within Logan Canyon head (eastern Canada) to provide a detailed, modern record of facies associated with hydrodynamic processes in a canyon head. These results suggest that bottom currents are responsible for maintaining gullies on canyon sidewalls and an axial channel on the canyon floor. Thin sections of sediment cores reveal that muddy sand in the canyon head consists of mud aggregates and silt and fine‐grained sand, both behaving similarly in terms of flow dynamics. Three facies are present at macro‐scale and micro‐scale: laminated, partially laminated and bioturbated sandy mud. Sedimentary structures include rhythmic sand and mud aggregate couplets, planar to wavy laminations, current ripple cross‐laminations and fining‐upward successions, which are attributed to bottom currents induced by internal tides. Bioturbated facies, characterized by discrete biogenic structures and cross‐cutting relationships, predominate and overprint a mottled background. A mottled bioturbation fabric also alternates with or locally disrupts layering within the partially laminated facies. Internal tide currents, capable of bedload transport and forming ripples, were measured during a monitoring period in the canyon head, followed by rapid re‐establishment of benthos and associated biogenic structures, supporting the core interpretations. Preservation of sedimentary facies associated with these internal tides occurs when the sedimentation rate outpaces the rate of bioturbation, likely during stormier conditions on the shelf. These results represent observations of sedimentary facies associated with modern bottom currents and internal tides, and can be used to interpret similar occurrences within the rock record. [ABSTRACT FROM AUTHOR]

Most existing path following control approaches for underactuated Autonomous underwater vehicles (AUVs) assumed that the roll angle can be passively stabilized at zero, and simplified the problem to the 5‐degree‐of‐freedom model. However, in practice, the roll motion suffers from undesirable oscillations induced by environmental disturbances, unbalanced propeller torque, etc. To preserve the stability of the system under nonzero roll dynamics, this paper proposes a novel line‐of‐sight‐ (LOS) guided path following control approach. Firstly, the spatial path following error dynamics model is derived to comprehensively reveal the negative effect of roll dynamics. Secondly, an improved LOS guidance law is designed to compensate for the nonzero roll angle. A salient feature is that the proposed guidance law can be applied to the underwater vehicle no matter it is underactuated in the roll motion or not. Thirdly, by assigning the guidance angles to the desired attitude angles, a robust attitude tracking controller is developed, in which the adaptive estimation technique is employed to handle the lumped uncertainties. The tanh‐based saturation function is incorporated such that the resultant control signals are inherently bounded. The closed‐loop path following and attitude tracking errors are proved to be globally asymptotically stable at the origin. Comparative simulation results are provided to substantiate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect. [ABSTRACT FROM AUTHOR]

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture. [ABSTRACT FROM AUTHOR]

Unmanned underwater vehicles (UUVs) are key technologies to conduct preventive inspection and maintenance tasks in offshore renewable energy plants. Making such vehicles autonomous would lead to benefits such as improved availability, cost reduction and carbon emission minimization. However, some technological aspects, including the powering of these devices, remain with a long way to go. In this context, underwater wireless power transfer (UWPT) solutions have potential to overcome UUV powering drawbacks. Considering the relevance of this topic for offshore renewable plants, this work aims to provide a comprehensive summary of the state of the art regarding UPWT technologies. A technology intelligence study is conducted by means of a bibliographical survey. Regarding underwater wireless power transfer, the main methods are reviewed, and it is concluded that inductive wireless power transfer (IWPT) technologies have the most potential. These inductive systems are described, and their challenges in underwater environments are presented. A review of the underwater IWPT experiments and applications is conducted, and innovative solutions are listed. Achieving efficient and reliable UWPT technologies is not trivial, but significant progress is identified. Generally, the latest solutions exhibit efficiencies between 88% and 93% in laboratory settings, with power ratings reaching up to 1–3 kW. Based on the assessment, a power transfer within the range of 1 kW appears to be feasible and may be sufficient to operate small UUVs. However, work-class UUVs require at least a tenfold power increase. Thus, although UPWT has advanced significantly, further research is required to industrially establish these technologies. [ABSTRACT FROM AUTHOR]

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes. [ABSTRACT FROM AUTHOR]

Side‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community. [ABSTRACT FROM AUTHOR]

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images. [ABSTRACT FROM AUTHOR]

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods. [ABSTRACT FROM AUTHOR]

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness. [ABSTRACT FROM AUTHOR]

It has been demonstrated that sound waves carrying orbital angular momentum undergo frequency modulation after being reflected by a rotating object. In this paper, we, observed the rotational Doppler shift phenomenon of the acoustic vortex in an underwater environment by using a Fresnel-spiral zone plate for the construction of the focused acoustic vortex with controllable topological charge. The positive and negative rotation speeds of the spinning object are precisely determined with the rotational frequency shift and the acoustic vortex's topological charge. The rotational Doppler effect of acoustic vortex provides a possibility to improve the detection and recognition accuracy of Autonomous Underwater Vehicles with propellers. [ABSTRACT FROM AUTHOR]

One of the primary means of sea exploration is autonomous underwater vehicles (AUVs). To perform these tasks, AUVs must navigate the rough challenging sea environment. AUVs usually employ an inertial navigation system (INS), aided by a Doppler velocity log (DVL), to provide the required navigation accuracy. The DVL transmits four acoustic beams to the seafloor, and by measuring changes in the frequency of the returning beams, the DVL can estimate the AUV velocity vector. However, in practical scenarios, not all the beams are successfully reflected. When only three beams are available, the accuracy of the velocity vector is degraded. When fewer than three beams are reflected, the DVL cannot estimate the AUV velocity vector. For such situations, only model-based approaches have been proposed to estimate missing beams. This paper presents a data-driven approach, MissBeamNet, to regress the missing beams in partial DVL beam measurement cases. To that end, a deep neural network (DNN) model is designed to process the available beams along with past DVL measurements to regress the missing beams. The AUV velocity vector is estimated using the available measured and regressed beams. To validate the proposed approach, sea experiments were made with the "Snapir" AUV, resulting in an 11 h dataset of DVL measurements. Our results show that the proposed system can accurately estimate velocity vectors in situations of missing beam measurements. Our dataset and codebase implementing the described framework is available at: https://github.com/ansfl/MissBeamNet. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are currently one of the most intensively developing branches of marine technology. Their widespread use and versatility allow them to perform tasks that, until recently, required human resources. One problem in AUVs is inadequate navigation, which results in inaccurate positioning. Weaknesses in electronic equipment lead to errors in determining a vehicle's position during underwater missions, requiring periodic reduction of accumulated errors through the use of radio navigation systems (e.g., GNSS). However, these signals may be unavailable or deliberately distorted. Therefore, in this paper, we propose a new computer vision-based method for estimating the position of an AUV. Our method uses computer vision and deep learning techniques to generate the surroundings of the vehicle during temporary surfacing at the point where it is currently located. The next step is to compare this with the shoreline representation on the map, which is generated for a set of points that are in a specific vicinity of a point determined by dead reckoning. This method is primarily intended for low-cost vehicles without advanced navigation systems. Our results suggest that the proposed solution reduces the error in vehicle positioning to 30–60 m and can be used in incomplete shoreline representations. Further research will focus on the use of the proposed method in fully autonomous navigation systems. [ABSTRACT FROM AUTHOR]

Vision-based autonomous underwater vehicle localization, navigation, and mapping is an attractive field of research in recent years. Despite the development of vision-only and vision-inertial-based algorithms, particularly in the terrestrial and aerial robotics groups, applying such approaches directly to underwater environments remains challenging due to visual degradation in the medium and further due to the slow crusade of the underwater vehicles. This paper introduces U-VIP-SLAM, a unique visual-inertial-pressure SLAM system that combines data from a monocular camera, a low-cost IMU, and a pressure sensor to create a SLAM system focused to the underwater region. All of the key components of a comprehensive SLAM system are included in the U-VIP-SLAM, including loop closure capabilities tailored to the underwater environment. Further, we introduce forward and backward projection strategies to fuse pressure measurement in the local bundle adjustment. Furthermore, a unique initialization strategy is proposed to effectively bootstrap the system by fusing pressure and IMU data. Several datasets were used to compare the performance of the proposed methods against the state-of-the-art monocular VI systems. Source Code is available at https://github.com/chintha/U-VIP-SLAM.git. [ABSTRACT FROM AUTHOR]

The use of Synthetic Aperture Sonar (SAS) in autonomous underwater vehicle (AUV) surveys has found applications in archaeological searches, underwater mine detection and wildlife monitoring. However, the easy confusability of natural objects with the target object leads to high false positive rates. To improve detection, the combination of SAS and optical images has recently attracted attention. While SAS data provides a large-scale survey, optical information can help contextualize it. This combination creates the need to match multimodal, optical–acoustic image pairs. The two images are not aligned, and are taken from different angles of view and at different times. As a result, challenges such as the different resolution, scaling and posture of the two sensors need to be overcome. In this research, motivated by the information gain when using both modalities, we turn to statistical exploration for feature analysis to investigate the relationship between the two modalities. In particular, we propose an entropic method for recognizing matching multimodal images of the same object and investigate the probabilistic dependency between the images of the two modalities based on their conditional probabilities. The results on a real dataset of SAS and optical images of the same and different objects on the seafloor confirm our assumption that the conditional probability of SAS images is different from the marginal probability given an optical image, and show a favorable trade-off between detection and false alarm rate that is higher than current benchmarks. For reproducibility, we share our database. [ABSTRACT FROM AUTHOR]

This study focuses on the inversion of sub-bottom profile (SBP) data in the northern South China Sea using an empirical relationship derived from sediment acoustic data. The sub-bottom profile is primarily utilized for various marine applications, such as geological mapping and resource exploration. In this research, we present a study conducted in the northern slope canyon of the South China Sea. Firstly, we obtained the seabed reflection coefficient from sub-bottom profiles obtained by the autonomous underwater vehicle (AUV) detection system. Secondly, we utilized the acoustic empirical relationship in the northern South China Sea to establish relationship equations between the seabed reflection coefficient and the porosity, density, and average particle size of the sediment at a main frequency of 4 kHz (the AUV shallow profile main frequency). Then, using these equations, we were able to invert the physical parameters such as porosity, density, and average particle size of the seabed surface sediments. Finally, the inverted results are compared and analyzed by using the sediment samples test data. The overall deviation rate of the inverted physical parameters is within the range of ±10% when compared. The inverted results closely match the measured values, accurately reflecting the dynamic changes in the physical properties of seabed surface sediments. Notably, the average grain size is a direct indicator of the sediment particles size with smaller particles found in deeper water. The variation characteristics of sediment physical parameters align well with the variation of sediment types in the canyon, which is consistent with changes in the water depth, topography, and hydrodynamic conditions of the area. This further demonstrates the reliability of the inversion results. [ABSTRACT FROM AUTHOR]

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme. [ABSTRACT FROM AUTHOR]

An external airbag-type electrodynamic transducer structure was proposed to solve the problems of traditional electrodynamic transducers, such as limited operating depth, failure to work for a long time, and excessive buoyancy change. The driving vibrator of the transducer was placed in the oil-filled cavity to balance the hydrostatic pressure and increase the heat dissipation channel of the coil. The external sound insulation airbag was used to eliminate the dipole's acoustic radiation mode to improve the transducer's acoustic radiation ability. This study analyzed the acoustic radiation performance of the transducer and the influencing law of operating depth on the acoustic radiation performance based on an equivalent circuit containing the acoustic parameters of the airbag. Moreover, the heat dissipation capabilities of the new and old electrodynamic transducer structures were compared through thermal simulation analysis. Then, two experimental prototypes of new and old electrodynamic transducers were fabricated. The source-level test results show that, within the core operating frequency band, the two electrodynamic transducers exhibit equivalent acoustic radiation performance. The external airbag-type electrodynamic transducer has long-time deepwater operation ability, and the buoyancy change can be controlled within an acceptable range of autonomous underwater vehicle thanks to the small airbag. [ABSTRACT FROM AUTHOR]

This study proposes a simultaneous localization method of a group of autonomous underwater vehicles (AUVs) based on one-way signals to realize distributed oceanographic surveys. Each AUV group consists of a single high-performance AUV (parent AUV) and the other AUVs (child AUVs). The child AUVs estimate their states (position and heading) based on the parent AUV as a positioning reference. By assuming a situation in which many AUV groups are deployed, the child AUVs can receive positioning signals from multiple parent AUVs. Although only the direction information of the parent AUV can be obtained from a signal from one parent AUV, the child AUVs can estimate their states by receiving signals from different parent AUVs. Sea experiments were conducted using an autonomous buoy and an AUV. The effectiveness of the proposed method was evaluated through a navigation simulation based on the sensor data obtained from the experiments. [ABSTRACT FROM AUTHOR]

Gravity on Earth is of great interest in geodesy, geophysics, and natural resource exploration. Ship-based gravimeters are a widely used instrument for the collection of surface gravity field data in marine regions. However, due to the considerable distance from the sea surface to the seafloor, the spatial resolution of surface gravity data collected from ships is often insufficient to image the detail of seafloor geological structures and to explore offshore natural minerals. Therefore, the development of a mobile underwater gravimetry system is necessary. The GraviMob gravimeter, developed for a moving underwater platform by Geo-Ocean (UMR 6538 CNRS-Ifremer-UBO-UBS), GeF (UR4630, Cnam) and MAPPEM Geophysics, has been tested over the last few years. In this study, we report on the high-resolution gravity measurements from the GraviMob system mounted on an Autonomous Underwater Vehicle, which can measure at depths of up to several kilometres. The dedicated GraviMob underwater gravity measurements were conducted in the Mediterranean Sea in March 2016, with a total of 26 underwater measurement profiles. All these measurement profiles were processed and validated. In a first step, the GraviMob gravity measurements were corrected for temperature based on a linear relationship between temperature and gravity differences. Through repeated profiles, we acquired GraviMob gravity measurements with an estimated error varying from 0.8 to 2.6 mGal with standard deviation after applying the proposed temperature correction. In a second step, the shipborne gravity data were downward continued to the measurement depth to validate the GraviMob measurements. Comparisons between the corrected GraviMob gravity anomalies and downward continued surface shipborne gravity data revealed a standard deviation varying from 0.8 to 3.2 mGal and a mean bias value varying from −0.6 to 0.6 mGal. These results highlight the great potential of the GraviMob system in measuring underwater gravity. [ABSTRACT FROM AUTHOR]

This article applies novel results for infinite- and finite-horizon optimal control problems with nonlinear dynamics and constraints. We use the Valentine transformation to convert a constrained optimal control problem into an unconstrained one and show uniqueness of the value function to the corresponding Hamilton–Jacobi–Bellman (HJB) equation. From there, we show how to approximate the solution of the initial (in)finite-horizon problem with a family of solutions that is Γ -convergent. Optimal solutions are efficiently obtained via a solver based on Pontryagin's Principle (PP). The proposed methodology is demonstrated on the path planning problem using the full nonlinear dynamics of an unmanned aerial vehicle (UAV) and autonomous underwater vehicle (AUV) involving state constraints in 3D environments with obstacles. [ABSTRACT FROM AUTHOR]

Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory. [ABSTRACT FROM AUTHOR]

This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Exploring underwater acoustic sensing networks to detect and monitor ocean characteristics provides a novel approach for marine environment monitoring, disaster warning and resource exploration. However, factors such as underwater acoustic communication, ocean currents and sensor power prevent the direct adoption of terrestrial wireless sensing networks. Moreover, most previous studies have simply increased the number of nodes while ignoring the interference caused by high node density. In this paper, data collection in an offshore tidal scenario is investigated by table forwarding based election (TFBE) scheme. To balance node energy and reduce energy consumption of autonomous underwater vehicles (AUVs), the improved algorithm (TFBE-A) is proposed. Meanwhile, in this scheme, we adopt AUVs to assist in collecting and put forward a 3D precision adjustable trajectory planning (3DPATP) algorithm. To better perform 3DPATP, the maximum minimum error estimation algorithm is presented to simplify the computer operations. Additionally, we study the probability distribution function of nodes in space, providing a foundation for arranging nodes and optimizing topology. Simulation results indicate that our algorithm outperforms compared strategies, particularly in latency and energy consumption. [ABSTRACT FROM AUTHOR]

The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme. [ABSTRACT FROM AUTHOR]

Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs), and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as biodiversity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area. [ABSTRACT FROM AUTHOR]

This study aims to address the three-dimensional path planning problem of autonomous underwater vehicle (AUV) in the environment of ocean current and seabed terrain obstacles, based on five biological swarm intelligent algorithm. Firstly, a three-dimensional seabed environment model and a Lamb vortex current environment model are established. Subsequently, a three-dimensional path planning mathematical model is established by considering the navigation distance, seabed terrain constraints and ocean current constraints. Furthermore, five biological swarm intelligent optimization algorithms are applied to solve the multi-objective nonlinear optimization problem. Finally, the experimental results show that the optimal path performance of the particle swarm optimization (PSO) algorithm is better than other algorithms. The planning speed of PSO algorithm is the fastest, and the robustness of WPA algorithm is the best. However, the planning time is the longest. The PSO algorithm is more suitable for three-dimensional path planning of AUV under the influence of seabed terrain obstacles and ocean currents. [ABSTRACT FROM AUTHOR]

In the complex ocean environment, the autonomous underwater vehicle (AUV) is subject to the multiple uncertainties including deterministic uncertainties and stochastic uncertainties, which greatly degrade the control performance. To solve this problem, a novel robust control strategy using combined uncertainty and disturbance estimator (UDE) and unscented Kalman filter (UKF) is proposed for the path-following control (PFC) of the underactuated AUV under multiple uncertainties in this paper. First, the UDE technique is adopted to handle the deterministic uncertainties, including deterministic components of environmental disturbances, parameter uncertainties, and unmodeled dynamics, etc. Second, the uncertainty of unactuated lateral channel, which cannot be dealt with by the UDE, is treated as an unknown parameter. Also, to tackle the stochastic uncertainties, which is often ignored in previous studies, including stochastic components of environmental disturbances, measurement noise, and the estimation errors of UDE which are treated as the process noise, the augmented UKF technique is adopted to jointly estimate the system states and the lateral channel unknown parameter. Finally, extensive numerical simulations and comparative analyses are presented to verify the efficiency and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Summary: Internet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi‐Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED‐CARP. Simulation results validate that proposed ED‐CARP can decrease the communication cost and increase the network lifetime. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicle (AUV) mapping of the western Rio Grande Rise (RGR), South Atlantic, and subsequent exploration and photography of horizontal lava flows exposed in near vertical, faulted escarpments, showed occurrences of red clays/weathered volcanic tops trapped between successive alkaline lava flows. These red clays indicate a hiatus in successive volcanic eruptions. Here, we report detailed mineralogical, geochemical, and rock magnetic characteristics of one such distinct red clay dredged from ~ 650 m water depth in the western RGR. The mineral constituents of the red clay are kaolinite, magnetite, oxidized magnetite (/maghemite), hematite, and goethite, with biogenic calcite and halite occupying voids or precipitated on the surface of the red clay. The chemical index of alteration (CIA) has a value of 93, showing that red clay is a product of extreme chemical weathering of the lava flows. The alkaline volcanic rocks recovered from nearby show an age of ~ 44 Ma, indicating an Eocene age for the volcanism. We show that the red clays are a product of sub-aerial chemical weathering of these Eocene volcanic rocks, in a warm-wet climate, before the thermal subsidence of the RGR to its modern-day bathymetric depth. [ABSTRACT FROM AUTHOR]

This report summarizes the recent research projects carried out at the Collaborative Robotics and Adaptive Machines Laboratory, Old Dominion University. The topics include collaborative robotics strategies for non-repetitive micro-drilling tasks, hand-held robots for spillage-free specimen retrieval tasks, development of a three-dimensional simulator for autonomous underwater vehicles, and enhancing search efficiency of glowworm-inspired robotic swarms for multiple source localization tasks. Research in these areas has applications in autonomous systems, advanced manufacturing, and health care. [ABSTRACT FROM AUTHOR]

Observations from a Seaglider, two pressure-sensor-equipped inverted echo sounders (PIESs), and a thermistor chain (T-chain) mooring were used to determine the waveform and timing of internal solitary waves (ISWs) over the continental slope east of Dongsha Atoll. The Korteweg–de Vries (KdV) and Dubreil–Jacotin–Long (DJL) equations supplemented the data from repeated profiling by the glider at a fixed position (depth ∼1017 m) during 19–24 May 2019. The glider-recorded pressure perturbations were used to compute the rarely measured vertical velocity (w) with a static glider flight model. After removing the internal tide–caused vertical velocity, the w of the eight mode-1 ISWs ranged from −0.35 to 0.36 m s−1 with an uncertainty of ±0.005 m s−1 due to turbulent oscillations and measurement error. The horizontal velocity profiles, wave speeds, and amplitudes of the eight ISWs were further derived from the KdV and DJL equations using the glider-observed w and potential density profiles. The mean speed of the corresponding ISW from the PIES deployed at ∼2000 m depth to the T-chain moored at 500 m depth and the 19°C isotherm displacement computed from the T-chain were used to validate the waveform derived from KdV and DJL. The validation suggests that the DJL equation provides reasonably representative wave speed and amplitude for the eight ISWs compared to the KdV equation. Stand-alone glider data provide near-real-time hydrography and vertical velocities for mode-1 ISWs and are useful for characterizing the anatomy of ISWs and validating numerical simulations of these waves. Significance Statement: Internal solitary waves (ISWs), which vertically displace isotherms by approximately 100 m, considerably affect nutrient pumping, turbulent mixing, acoustic propagation, underwater navigation, bedform generation, and engineering structures in the ocean. A complete understanding of their anatomy and dynamics has many applications, such as predicting the timing and position of mode-1 ISWs and evaluating their environmental impacts. To improve our understanding of these waves and validate the two major theories based on the Korteweg–de Vries (KdV) and Dubreil–Jacotin–Long (DJL) equations, the hydrography data collected from stand-alone, real-time profiling of an autonomous underwater vehicle (Seaglider) have proven to be useful in determining the waveform of these transbasin ISWs in deep water. The solutions to the DJL equation show good agreement with the properties of mode-1 ISWs obtained from the rare in situ data, whereas the solutions to the KdV equation underestimate these properties. Seaglider observations also provide in situ data to evaluate the performance of numerical simulations and forecasting of ISWs in the northern South China Sea. [ABSTRACT FROM AUTHOR]

In this paper we present numerical methods for the approximation of nonlinear Itô stochastic differential equations on manifolds. For this purpose, we extend Runge-Kutta–Munthe-Kaas (RKMK) schemes for ordinary differential equations on manifolds to the stochastic case and analyse the strong convergence of these schemes. Since these schemes are based on the application of a stochastic Runge-Kutta (SRK) scheme in a corresponding Lie algebra, we address the question under which circumstances the stochastic RKMK method has the same strong order of convergence as the applied SRK scheme. To illustrate our answer to this question and the effectiveness of our schemes, we show some numerical results of applying these methods to a problem with an autonomous underwater vehicle. [ABSTRACT FROM AUTHOR]

In this paper, the desired yaw orientation for an autonomous underwater vehicle is attained by acquiring a cascaded control structure based on a robust optimal control algorithm with a backstepping approach. A robust state feedback optimal control law is designed to control the yaw rate. Hence, the desired yaw rate is intended to be obtained by the backstepping controller by controlling the desired yaw orientation. The implementation of the proposed robust control algorithm is formulated by using semi-definite programming. A linear quadratic regulator in terms of linear matrix inequality is designed to address the control problem. The design of robust optimal control law in the steering plane is achieved by considering an uncertain polytopic AUV system. Realization of the proposed control algorithm is conducted in MATLAB/Simulink environment using the YALMIP tool. Robust behavior is ensured by the proposed control algorithm while tracking the desired yaw. The robustness analysis is extended by considering the various ranges of specific uncertain parameters to highlight the efficacies of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Due to the unavoidable movement of nodes within oceanic environments and the continual changing of underwater sound speed, it is difficult to synchronize clocks between nodes and locate them. Therefore, this paper proposes a joint time synchronization and localization mechanism (JTSL). Firstly, a time synchronization method is proposed based on three autonomous underwater vehicles (AUVs). In this method, AUVs send synchronization request signals to the moving unknown node three times. The clock skew and offset of each AUV compared to the clock of the unknown node are obtained based on the information transfer delay between nodes as sound speed changes. After time synchronization, the unknown node receives location information from AUVs and obtains the sound speed change function. Based on this function and kinematic equations, a distance calculation method is developed to calculate the distance between the unknown node and AUVs. In addition, a multi-layer fast meshing localization method is designed to improve the accuracy and speed of node localization. The simulation experiments show that JTSL can overcome difficulties related to time synchronization and localization of mobile nodes, and it demonstrates excellent performance in terms of time synchronization accuracy, energy efficiency, localization accuracy, and localization time. [ABSTRACT FROM AUTHOR]

Mesoscale eddies help regulate ocean energy cascades. Eddies deformation influences barotropic instability, which represents kinetic energy transfer between scales; however, the barotropic instability structure has not been well studied. We investigated an intra‐thermocline eddy (ITE) and developed a novel anisotropic method to examine the horizontal barotropic instability. The development of the ITE was monitored using a state‐of‐the‐art autonomous underwater vehicle from May to July. The ITE became trapped in June and moved eastward in July. Based on anisotropic theory, the barotropic instability was separated into isotropic and anisotropic productions. The anisotropy contained information regarding shape and mean flow feedback of the eddy. Barotropic instability was the main source for ITE eastward propagation and was dominated by anisotropic production. Following a shape and anisotropy change, the ITE gained the mean‐flow kinetic energy by the anisotropy shear production in June and by the anisotropy stretch production when moving eastward in July. Plain Language Summary: Mesoscale eddies play vital roles in ocean circulation and are important in energy cascades between large‐scale ocean circulation and dissipate scales. The barotropic instability could induce kinetic energy transition between scales, however, the underlying mechanism has not been well studied. We developed a novel method to decompose the horizontal barotropic instability into isotropic production and anisotropic production. The development of a mesoscale eddy was monitored continuously using a state‐of‐the‐art autonomous underwater vehicle. This mesoscale eddy became stationary in June and began to move eastward in July. In the observation case, the barotropic instability mainly controlled the eddy kinetic energy budget and is dominated by anisotropic production, indicating the contribution of the mean‐flow strain. We found that, the mesoscale eddy gained the mean‐flow kinetic energy via the anisotropy shear production when it was stationary, while via anisotropy stretch production when it moved eastward. Key Points: For 3 months, a state‐of‐the‐art AUV monitored the development of an intra‐thermocline eddy (ITE) in terms of vertical transectsNovel anisotropy analysis was developed and revealed interactions between eddy anisotropy and mean‐flow strain in barotropic instabilityThe anisotropic partitioning of the ITE at different stages helped gain kinetic energy from the mean flow [ABSTRACT FROM AUTHOR]

Abstract—In experiments on a stationary acoustic track under a solid ice cover, estimates of possible sound signal propagation time variations at distances of ∼4 km with a period of more than 100 s were obtained. The experiments were carried out on Lake Baikal in the spring period, when the vertical profile of the sound speed has two sections characteristic of freshwater areas: an upper layer with a near constant sound speed and a lower layer with linear growth of sound speed. Under these conditions, the variations of the propagation time did not exceed ~10–4 s. Numerical modeling showed that the variations of propagation times due to the variability of the medium are minimal for the case when the sound source and receiver are located in the upper layer. It is demonstrated that in this case it is acceptable to take the sound speed in the upper quasi-homogeneous layer as the effective value of the sound speed, which determines the propagation time. The obtained results allowed us to formulate recommendations on under-ice acoustic positioning of autonomous underwater vehicles. [ABSTRACT FROM AUTHOR]

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. [ABSTRACT FROM AUTHOR]

Underwater target detection technology plays a crucial role in the autonomous exploration of underwater vehicles. In recent years, significant progress has been made in the field of target detection through the application of artificial intelligence technology. Effectively applying AI techniques to underwater target detection is a highly promising area of research. However, the difficulty and high cost of underwater acoustic data collection have led to a severe lack of data, greatly restricting the development of deep-learning-based target detection methods. The present study is the first to utilize diffusion models for generating underwater acoustic data, thereby effectively addressing the issue of poor detection performance arising from the scarcity of underwater acoustic data. Firstly, we place iron cylinders and cones underwater (simulating small preset targets such as mines). Subsequently, we employ an autonomous underwater vehicle (AUV) equipped with side-scan sonar (SSS) to obtain underwater target data. The collected target data are augmented using the denoising diffusion probabilistic model (DDPM). Finally, the augmented data are used to train an improved YOLOv7 model, and its detection performance is evaluated on a test set. The results demonstrate the effectiveness of the proposed method in generating similar data and overcoming the challenge of limited training sample data. Compared to models trained solely on the original data, the model trained with augmented data shows a mean average precision (mAP) improvement of approximately 30% across various mainstream detection networks. Additionally, compared to the original model, the improved YOLOv7 model proposed in this study exhibits a 2% increase in mAP on the underwater dataset. [ABSTRACT FROM AUTHOR]

Multiple autonomous underwater vehicles (AUVs) have gradually become the trend in underwater operations. Identifying and detecting these new underwater multi-targets is difficult when studying underwater moving targets. A 28-element transducer is used to test the echo of multiple AUVs with different layouts in a lake. The characteristics of the wideband echo signals are studied. Under the condition that the direction of arrival (DOA) is not known, an autofocus coherent signal subspace (ACCSM) method is proposed. The focusing matrix is constructed based on the received data. The spatial spectrum of the array signal of multiple AUVs at different attitudes is calculated. The algorithm estimates the DOA of the echo signals to overcome the shortcomings of traditional wideband DOA estimation and improve its accuracy. The results show that the highlights are not only related to the number of AUVs, but are also modified by scale and attitude. The contribution of the microstructure of the target in the overall echo cannot be ignored. Different parts of the target affect the number of highlights, thus resulting in varying numbers of highlights at different attitude angle intervals. The results have significant implications for underwater multi-target recognition. [ABSTRACT FROM AUTHOR]

Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods. [ABSTRACT FROM AUTHOR]

In this work, the output-feedback fault-tolerant tacking control issue for underactuated autonomous underwater vehicle (AUV) with actuators faults is investigated. Firstly, an output-feedback error tacking system is constructed based on the theoretical model of underactuated AUV with actuators faults. Then, an adaptive dynamic programming (ADP) based fault-tolerant control controller is developed. In our proposed control scheme, a neural-network observer is designed to approximate the system states with actuators faults. An online policy iteration algorithm is designed with critic network and action network in order to improve the tracking accuracy. Based on Lyapunov stability theorem, the stability of the error tracking system is guaranteed by the proposed controller. At last, the simulation results show that the underactuated AUV achieves better tracking performance. [ABSTRACT FROM AUTHOR]