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In this work, consideration is given to the kinetic energy recovery of a buoyancy engine and with particular reference to gliders. Utilizing a simplified physical model, an expression for the efficiency of kinetic energy recovery in terms of reduction of the required energy input from batteries was derived. [ABSTRACT FROM AUTHOR]

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

A rapid stability dimensionality‐augmented state observer (RSDASO) based event‐driven control strategy is presented for the autonomous underwater vehicle (AUV) trajectory tracking issue, addressing state constraints, model uncertainty, limited communication resources and unknown external time‐varying disturbances. The first step is to develop a fast stability extended state observer to estimate the lumped disturbances and unmeasurable states of the system and ensure the estimation error converges in fixed time. Secondly, a fixed‐time AUV trajectory tracking control method is proposed to ensure that the tracking error of the system converges within a fixed time, based on the mentioned observer. Simultaneously, to reduce the communication resource usage by the system, an event‐triggered mechanism (ETM) is included in the control law. Lastly, simulation experiments verify the effectiveness of the process. [ABSTRACT FROM AUTHOR]

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed. [ABSTRACT FROM AUTHOR]

Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the high absorption of radio-frequency signals underwater and the absence of a global navigation satellite system (GNSS). As a result, most navigation algorithms rely on inertial and acoustic signals; precise localization is then costly in addition to being independent from acoustic data communication. The purpose of this paper is to propose and analyze the performance of a novel low-cost simultaneous communication and localization algorithm. The considered scenario consists of an AUV that acoustically sends sensor or status data to a single fixed beacon. By estimating the Doppler shift and the range from this data exchange, the algorithm can provide a location estimate of the AUV. Using a robust state estimator, we analyze the algorithm over a survey path used for AUV mission planning both in numerical simulations and at-sea experiments. [ABSTRACT FROM AUTHOR]

A new autonomous underwater vehicle (AUV) has high maneuverability near the bottom and a direction turnaround ability, called the autonomous underwater helicopter (AUH). This paper numerically investigates the hydrodynamic performance of the AUH. A Reynolds-Averaged Navier–Stokes (RANS) equation, a computational fluid dynamics (CFD) technique, is applied to analyze the AUH's behavior. Investigations of the AUH's hydrodynamic characteristics become more obvious with a service speed in the range of 0.4–1.2 m/s. For the same velocity condition, the resistance of the AUH increases, and the irregular eddy at the rear of the AUH expands with changes in the angles of attack and the length/height ratio. Essential design characteristics including pressure, velocity distribution, and velocity streamlines are shown and analyzed. These insights can be used as a guideline to reduce drag force and optimize the AUH profile for future designs. It has great potential for improving the AUH's control algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel tube model predictive control approach for dynamic positioning of autonomous underwater vehicles with state and input constraints. The cost function is selected as a mixture of weighted 1-norms and ∞-norms of states and inputs, which can be reconstructed into linear summation form. Specially, a method is presented to calculate the weighted matrix of the terminal cost so that closed-loop stability is achieved. The proposed control problem can be solved by linear programming, which provides high computational efficiency. Experimental results on “UVIC-I” AUV are conducted to validate the viability and validity of the presented method. [ABSTRACT FROM AUTHOR]

The optimization of the desired autonomous underwater vehicle (AUV) trajectory modeling and AUV trajectory tracking control in the benthonic hydrothermal area were studied. In the conventional trajectory tracking model construction methods, the time points were roughly combined with the position points of the planned path, making it difficult to produce a smooth trajectory. Although the spline interpolation method was an ideal option for smooth curves, a great number of points were needed for a complex desired trajectory mode. In response to the demanding requirements of AUV trajectory tracking control in the benthonic hydrothermal area, an under-actuated test platform was first established, and the cubic spline interpolation was adopted to process the preset path points for a smooth desired trajectory. An improved slime mold algorithm (SMA) was put forward to optimize the interpolating points used in the trajectory modeling. The Levy flight technology and the compactness technique to speed up the search process and increase the search accuracy. The simulation experiments were conducted in comparison with the artificial fish swarm algorithm (AFSA), the particle swarm optimization (PSO), and the compact cuckoo search (CCS). The results showed that the improved SMA shortened the search process, effectively avoided the local extreme values, and generated a high-precision desired trajectory model in a shorter time. The pool test also verified the feasibility and effectiveness of the proposed method. The method proposed in this study can satisfy the modeling of benthonic hydrothermal trajectory with a fewer number of nodes, faster search progress and search accuracy. [ABSTRACT FROM AUTHOR]

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of controlling a DC propulsion motor of an autonomous underwater vehicle using a bidirectional non-isolated DC-DC converter. An automatic speed control system with a parametric controller was developed in the Matlab / Simulink package. To reduce energy conversion stages, dc-dc converters are often used to control dc motors AUV. The result of the work is an adaptive control system for the speed of the AUV propeller, which would provide high accuracy for a wide range of speeds and at the same time limit the maximum current and voltage at the armature. The indicators of the quality of performance in various modes of operation were determined, maps of the controller settings were compiled, and parametric correction was introduced. The operation of the system is analyzed and drawbacks are eliminated, such as voltage surges when switching from one speed to another, a decrease in overshoot and settling time. The performance of the system is analyzed under the conditions of random inputs from the propeller. The description of the laboratory stand for the study of the control system is given. A description of the laboratory study of the operation of the "DC converter—engine" system under conditions of random fluctuations in the moments on the motor shaft is given. It is shown that the proposed system has shown itself well under laboratory conditions and the experimental results are consistent with modeling in Matlab / Simulink.Article Highlights: To developing new architectures for the AUV propulsion system, the underlying question is to choose the most energy-efficient algorithm for the operation of the control system. Further research will be aimed at improving the laboratory plant in terms of reducing ripple voltage, control accuracy and signal filtering. Tests were carried out for various modes of operation of the propeller drive. The processed laboratory test results were compared with simulation results. They demonstrated that the control system got well with the speed control. [ABSTRACT FROM AUTHOR]

This paper investigates robust H ∞ -based control for autonomous underwater vehicle (AUV) systems under time-varying delay, model uncertainties, and cyber-attacks. Sensor and actuator cyber-attacks can cause faults in the overall AUV system. In addition, the behavior of the system can be affected by the presence of complexities, such as unknown random uncertainties that occur in system modeling. In this paper, the robustness against unpredictable random uncertainties is investigated by considering unknown but norm-bounded (UBB) random uncertainties. By constructing a proper Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) techniques, new stability criteria in the form of LMIs are derived such that the AUV system is stable. Moreover, this work is novel in addressing robust H ∞ control, which considers time-varying delay, cyber-attacks, and randomly occurring uncertainties for AUV systems. Finally, the effectiveness of the proposed results is demonstrated through two examples and their computer simulations. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV's dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments. [ABSTRACT FROM AUTHOR]

This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks. [ABSTRACT FROM AUTHOR]

The object of this study is the process of generating an appropriate response by an intelligent agent when detecting and tracking an underwater electrical cable using a decision support system. The goal of the work is to develop the architecture of a decision support system for diagnosing underwater electrical cables and the main algorithms for its operation. To achieve the research goal, intelligent agents with a complex IPK architecture, which includes information, preferences (rules), and knowledge, were used. The combination of the structure of intelligent agents and the advantages of hierarchical knowledge bases allowed for the natural language representation of knowledge. The functional core of the system consists of four main agents, one of which facilitates the interaction between the user and the surrounding environment. To address the uncertainty in the cable's position on the seabed, the capabilities of fuzzy sets, describing its feature space with membership functions, were employed. The most significant results involve planning for the detection and tracking of underwater electrical cables, taking into account past decision-making experiences in similar cases and adapting them to the current situation through case-based reasoning. The importance of the obtained results lies in providing the decision-maker with a possible option for deploying an underwater vehicle based on accepted logic and rules for detecting underwater electrical cables. Further research is focused on implementing automatic target selection for intervention and developing a method for automatic modification of reasoning methods, preference rule bases, and operational knowledge.

In this research, we explore a finite horizon optimal stabilization and tracking control scheme for the dynamical model of a 6‐DOF Autonomous Underwater Vehicle (AUV). Dynamical equations of the AUV are represented in a Lie group (SE(3)$$ SE(3) $$) framework, encompassing both translational and rotational motions. Utilizing a left Lie group action on SE(3)$$ SE(3) $$, we define error function for velocities via a right transport map to effectively address optimal trajectory tracking. The optimal control objective is formulated as a trade‐off problem, aiming to minimize both errors and control effort simultaneously. Left action on SE(3)$$ SE(3) $$ yields the left trivialized Hamiltonian function from which the concomitant state and costate dynamical equations are derived using Pontryagin's Minimum Principle (PMP). Consequently, the resulting two‐point boundary value problem is solved to obtain optimal trajectories. We demonstrate the optimality of the resulting solution obtained from the derived control law. For ensuring boundedness in the presence of small disturbances, this study incorporates the effects of internal parametric uncertainties associated with added mass and inertia components, along with the influence of external disturbances induced by ocean currents. Through simulation validations, we confirm the alignment of our results with the theoretical developments, demonstrating that the proposed control law effectively mitigates both parametric uncertainties and ocean current disturbances. [ABSTRACT FROM AUTHOR]

Terrain‐aided navigation (TAN) is a viable method to achieve long‐term underwater navigation for long‐range autonomous underwater vehicles (AUVs). However, the high‐accuracy positioning results of most TAN systems rely on precise a priori seabed terrain maps, which restricts their applicability to a few areas with accurate bathymetric measurements of the seabed terrain. This article introduces a TAN system based on the General Bathymetric Chart of the Oceans (GEBCO) data set for global marine applications. Specifically, to address the low accuracy and poor robustness of the TAN system with imprecise bathymetric measurement and low‐resolution data from the GEBCO data set, this article proposes a multizonotope TAN method based on set‐membership filter (SMF) theory. The SMF theory is employed to handle the unknown distribution of the measurement noise from the GEBCO data set, introducing a multizonotope measurement update model to achieve more precise navigational results while addressing the perceptual ambiguity caused by self‐similar terrain. The smoothness of the terrain is incorporated as a parameter in the generation ranges of multizonotope, enabling adaptive adjustment based on terrain smoothness to reduce costs and enhance navigational performance. The accuracy and robustness of the proposed method are verified through all shipboard experiments, publicly available data sets, and AUV experiments. Compared with state‐of‐the‐art TAN methods, the average and maximum positioning errors have decreased by 64.83% and 48.84%, respectively. Finally, based on the experimental results, a preliminary distribution of suitable areas in the oceans is provided. [ABSTRACT FROM AUTHOR]

Given the demanding and unpredictable operational conditions, autonomous underwater vehicles (AUVs) often encounter different propulsion faults, leading to significant economic losses and mission impairments. To address this challenge, vibratory time‐series features can be extracted for the precise propulsion fault diagnosis of AUVs. A squeeze‐and‐excitation (SE) attention residual network (SEResNet) is therefore put forward to enhance the feature extraction for AUV propulsion fault diagnosis. By leveraging the vibratory time‐series data obtained from the AUV, an SE attention mechanism is embedded into a residual network. This integration facilitates the extraction of pertinent vibratory fault features, subsequently utilized for accurate diagnosis of any propulsion faults. The effectiveness of the proposed SEResNet was validated through its application to an actual experimental AUV, with comparison against the state‐of‐the‐arts. The results reveal that the present SEResNet outperforms all other comparison methods in terms of diagnosis performance for AUV propulsion faults. [ABSTRACT FROM AUTHOR]

Abstract The paper deals with the problem of controlling a DC propulsion motor of an autonomous underwater vehicle using a bidirectional non-isolated DC-DC converter. An automatic speed control system with a parametric controller was developed in the Matlab / Simulink package. To reduce energy conversion stages, dc-dc converters are often used to control dc motors AUV. The result of the work is an adaptive control system for the speed of the AUV propeller, which would provide high accuracy for a wide range of speeds and at the same time limit the maximum current and voltage at the armature. The indicators of the quality of performance in various modes of operation were determined, maps of the controller settings were compiled, and parametric correction was introduced. The operation of the system is analyzed and drawbacks are eliminated, such as voltage surges when switching from one speed to another, a decrease in overshoot and settling time. The performance of the system is analyzed under the conditions of random inputs from the propeller. The description of the laboratory stand for the study of the control system is given. A description of the laboratory study of the operation of the "DC converter—engine" system under conditions of random fluctuations in the moments on the motor shaft is given. It is shown that the proposed system has shown itself well under laboratory conditions and the experimental results are consistent with modeling in Matlab / Simulink.

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime.

The object of the study is to enhance operational efficiency and reduce fuel consumption of autonomous underwater vehicles during the monitoring of underwater electrical and optical cables based on FCA under conditions of uncertainty. To achieve the goal of the research fuzzy cellular automata are used, combining the advantages inherent in traditional cellular automata and provided by the capabilities of fuzzy sets and fuzzy logic. The cable location uncertainty is caused by possible earthquakes, turbulent currents, random impacts of anchors or fishing gear, cable fouling by marine vegetation and terrorist attacks. The developed approach allows to synthesise a Pareto-optimal vehicle route along the estimated coordinates of the object of study, providing minimum fuel consumption for minimum cable inspection time and satisfying the given system of constraints. Formal models are based on the use of fuzzy cellular automata, which are used to describe the three-dimensional model of the operating environment, zones and objects that hinder or limit the movement, and the behaviour of the underwater vehicle. The most significant results are the formal description of the problem solution space, and the method of modelling the route of an autonomous underwater vehicle in space to improve the efficiency and quality of the solution of the problem of monitoring the state of the object of interest. The significance of the results obtained is the possibility of solving a complex multi-criteria optimisation problem of finding the route of an autonomous underwater vehicle to monitor the cable system in three-dimensional space.

The vision enhancement of autonomous underwater vehicle (AUV) has received increasing attention and rapid development in recent years. However, existing methods based on prior knowledge struggle to adapt to all scenarios, while learning-based approaches lack paired datasets from real-world scenes, limiting their enhancement capabilities. Consequently, this severely hampers their generalization and application in AUVs. Besides, the existing deep learning-based methods largely overlook the advantages of prior knowledge-based approaches. To address the aforementioned issues, a novel architecture called CodeUNet is proposed in this paper. Instead of relying on physical scattering models, a real-world scene vision enhancement network based on a codebook prior is considered. First, the VQGAN is pretrained on underwater datasets to obtain a discrete codebook, encapsulating the underwater priors (UPs). The decoder is equipped with a novel feature alignment module that effectively leverages underwater features to generate clean results. Then, the distance between the features and the matches is recalibrated by controllable matching operations, enabling better matching. Extensive experiments demonstrate that CodeUNet outperforms state-of-the-art methods in terms of visual quality and quantitative metrics. The testing results of geometric rotation, SIFT salient point detection, and edge detection applications are shown in this paper, providing strong evidence for the feasibility of CodeUNet in the field of autonomous underwater vehicles. Specifically, on the full reference dataset, the proposed method outperforms most of the 14 state-of-the-art methods in four evaluation metrics, with an improvement of up to 3.7722 compared to MLLE. On the no-reference dataset, the proposed method achieves excellent results, with an improvement of up to 0.0362 compared to MLLE. Links to the dataset and code for this project can be found at: https://github.com/An-Shunmin/CodeUNet. [ABSTRACT FROM AUTHOR]

Steering motion bestows autonomous underwater vehicles (AUVs) with the agility to navigate intricate paths and trajectories precisely. Ensuring effective steering position stabilization in underwater vehicles is paramount, as it enables precise navigation and enhances safety, efficiency, data accuracy, adaptability to changing conditions, and the overall success of diverse underwater missions. This article addresses the challenging task of steering position stabilization in underactuated AUVs. To achieve this, we employ an interconnection and damping assignment passivity‐based control method to design a control law tailored for steering position stabilization. Our approach considers the nonlinear dynamics of a six‐degrees‐of‐freedom steering motion in AUVs. The control objective involves assigning a suitable energy function and reshaping the interconnection and damping structure to render the closed‐loop system asymptotically stable at the desired equilibrium point. The robustness of our proposed control law is assessed rigorously, subjecting it to modeling uncertainties and underwater disturbances. Our findings are substantiated with simulation results that support the efficacy of the designed control law. Notably, we base our simulations on experimentally validated steering motion parameters obtained from the REMUS 100 AUV, enhancing the real‐world applicability of our research. [ABSTRACT FROM AUTHOR]

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime. [ABSTRACT FROM AUTHOR]

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method's effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path's safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas. [ABSTRACT FROM AUTHOR]

In Underwater Acoustic Sensor Networks (UASNs), optimizing energy efficiency and minimizing void occurrences in routing is paramount. Due to the energy constraints of sensor nodes, low-power transmission is essential for conserving energy. Previous research highlighted the effectiveness of clustering and routing to enhance energy efficacy in UASNs. Therefore, the clustering and routing processes can be considered as optimization problems that are nondeterministic polynomial-time (NP) hard. These challenges can be tackled through the application of machine learning algorithms and meta-heuristics. In this context, K-means clustering is employed to partition the network into clusters, designating the centroid as an ideal Cluster Head (CH) location. This ensures a one-hop proximity between the CH and cluster members, reducing transmitting power and enhancing network energy efficiency. Subsequently, a potential CH is selected using a marine predator optimization (MPA) algorithm based on the derived multi-objective fitness function. The MPA algorithm not only determines the optimal CH but also moves the elected CH to the K-means centroid location. Consequently, Autonomous Underwater Vehicles (AUVs) are utilized to collect and route packets from the CH to the Base Station (BS), minimizing the occurrence of void nodes and avoiding obstacle collisions. An optimal routing path for AUV is established through a way-point-based navigation scheme to achieve high packet reliability. Additionally, the proposed method (DCARo) dynamically determines the optimal number of clusters using the elbow method, ensuring scalability according to network size. Extensive simulations affirm the superiority of the DCARo across various performance metrics. [ABSTRACT FROM AUTHOR]

This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

Trajectory tracking for underactuated autonomous underwater vehicles (AUVs) is challenging due to coupling dynamics, modeling inaccuracies, and unknown disturbances. To tackle this, we propose a decoupling gaze-assisted prescribed performance controller (GAPPC). We first use an error transformation approach to achieve the prescribed performance, incorporating the line-of-sight (LOS) algorithm and an event-triggering mechanism to handle the kinematic characteristics of underactuated AUVs. Next, we develop a control strategy for the transformed error that does not require knowledge of the model parameters, including fast dynamic compensation to reduce steady-state errors. Finally, we analyze the controller's stability and present simulation results. Simulations, which account for modeling inaccuracies and unknown ocean currents, show that the GAPPC improves stability errors by 67.3% compared to the adaptive robust controller. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control challenge in the diving motion system of a specific class of autonomous underwater vehicles (AUVs) characterized by a torpedo-like shape. A decoupled and reduced-order three degrees-of-freedom linearized diving motion model is employed for depth position control. A control law is synthesized using the immersion and invariance (I&I) technique to achieve the control objectives. The primary aim is to attain tracking by immersing a stable, lower-order target (second-order) dynamic system into a three-dimensional manifold, upon which the closed-loop system evolves. We address the regulation problem as a specialized instance of the tracking problem, with the reference input set as a predetermined known depth that requires regulation. The efficacy of the proposed control law is evaluated through simulation studies involving various scenarios. Robustness tests are conducted to assess the control law’s performance under modeling uncertainties and underwater disturbances. The computer simulation employs an AUV named MAYA, utilizing experimentally validated diving motion parameters. A comparative analysis is performed between the proposed control law and other benchmark controllers to gauge its performance. Additionally, the effectiveness of the proposed control law is confirmed by validating its application to the nonlinear model of the diving motion system. [ABSTRACT FROM AUTHOR]

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations. [ABSTRACT FROM AUTHOR]

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane. • A novel ESO is developed to estimate the mismatched lumped disturbances and recover the unmeasured velocities by combining high-gain and singular perturbation techniques, leading to reduced complexity in stability analysis. • A DO is designed to estimate model uncertainties by utilizing the difference in bandwidths between the observer and state dynamics. The resulting singular perturbation analysis provides a new perspective on designing a DO in a straightforward manner. • An interconnected-system control law is proposed based on a time scale decomposition method to address the problem of the "explosion of terms" inherent in conventional backstepping. • The stability analysis enables the derivation of mathematical bounds on the control gains, offering guidance for selecting control gains to prevent controller ill-conditioning and/or closed-loop instability. [ABSTRACT FROM AUTHOR]

In this article, the hydrodynamic characteristics of Autonomous Underwater Vehicles (AUVs) are investigated and analyzed under the influence of rotating flow fields, which were generated in a recirculating water tank via a rotating propeller. Initially, experiments were carried out to measure flow field variables and quantities of Interest across the AUV in the presence of the rotating propeller while varying the rotational speed and the extent of rotational flow strength. The flow field across the AUV was measured using an Acoustic Doppler Velocimeter (ADV). These measured turbulent flow statistics were used to validate the Reynolds Stress Model (RSM) based numerical predictions in a commercial CFD solver. After preliminary validation of the turbulent flow statistics with the numerical predictions, a series of numerical simulations were performed to investigate the effect of the rotational flow field of the propeller on the drag, skin friction, and pressure coefficients of the AUV. The operating speed and location of the propeller were also varied to check their effects on the hydrodynamic performance of the AUV. The results provided in this article will be useful for the design optimization of AUVs cruising in shallow water where the flow is highly rotational because of wave-current interactions. Additionally, the results and analysis are relevant to study the design and operation of AUVs that have to operate in a group of unmanned underwater vehicles or near submarines and ships where the flow field is highly complex and such rotational effects are present. [ABSTRACT FROM AUTHOR]

Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm's optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV. [ABSTRACT FROM AUTHOR]

This paper investigates consensus control in heterogeneous and uncertain multiple autonomous underwater vehicle (AUV) systems under implicit reduced visibility conditions. We address challenges such as environmental uncertainties and system nonlinearity by utilizing a unified connectivity approach to model low-visibility interactions and heterogeneous multi-AUV dynamics. Our main contributions include developing a feedback linearization model for heterogeneous multi-AUV systems that accounts for uncertainties, introducing an adaptive consensus controller based on relative positioning that effectively manages implicit visual interaction limitations and validating our strategies through stability analysis and numerical simulations. Our simulations demonstrate approximately a 60% improvement in accuracy compared to previous algorithms, highlighting the practical value of our approach in AUV recovery operations. These advancements provide a robust solution for consensus control in complex underwater environments. [ABSTRACT FROM AUTHOR]

Introduction: Autonomous Underwater Vehicles (AUVs) are capable of independently performing underwater navigation tasks, with side-scan sonar being a primary tool for underwater detection. The integration of these two technologies enables autonomous monitoring of the marine environment. Methods: To address the limitations of existing seabed detection methods, such as insufficient robustness and high complexity, this study proposes a comprehensive seabed detection method based on a sliding window technique. Additionally, this study introduces a sonar image stitching method that accounts for variations in image intensity and addresses challenges arising from multi-frame overlaps and gaps. Furthermore, an autonomous target perception framework based on shadow region segmentation is proposed, which not only identifies targets in side-scan sonar images but also provides target height measurements. Results: Comprehensive seabed detection method improves accuracy by 31.2% compared to the peak detection method. In experiments, the height measurement error for this method was found to be 9%. Discussion: To validate the effectiveness of the proposed seabed detection method, sonar image stitching method, and target perception framework, comprehensive experiments were conducted in the Qingjiang area of Hubei Province. The results obtained from the lake environment demonstrated the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Aiming at the problems of incomplete path coverage and path redundancy in Autonomous Underwater Vehicle (AUV) path planning, an Ant Colony Path Planning Optimization Based on Opposition-Based Learning (ACPPO-OBL) is proposed. Firstly, Opposition-Based Learning (OBL) is introduced during the initialization phase of the ant colony. Moreover, the theoretical proof that ant colonies can be distributed near the optimal ant colony has also been proposed, indicating that the ACPPO-OBL algorithm has enhanced global search ability. Secondly, the coefficient for pheromone evaporation is revised. Besides, the proposed method involves a global pheromone update incorporating both best and worst reward mechanisms. Furthermore, it has been theoretically proven that the ACPPO-OBL algorithm has upper and lower bounds on the total pheromone concentration when searching for the optimal path. Additionally, an adaptive coefficient is incorporated into the heuristic function. The theoretical proof of the convergence of ACPPO-OBL has been established. As demonstrated in simulation experiments, ACPPO-OBL increases path coverage rates by 2–6 % and reduces path lengths by 6–11 % compared to ECDM planning. The ACPPO-OBL can be applied to cover irregular areas of various shapes and provides better coverage, improving the efficiency and stability of full-coverage paths in irregular areas. [ABSTRACT FROM AUTHOR]

This article proposes a new method for the synthesis of autonomous underwater vehicles (AUVs) with a multilink manipulators control system, which provides for the automatic execution of contact manipulation operations by AUVs in stabilized hovering mode near or above target objects. To achieve the desired magnitude of the working tool's force effect on the object surface, the force vector exerted by this tool is calculated. Next, control signals providing additional movements of the manipulator's tool in the direction of the desired force vector are generated. Simultaneously, based on the calculated effects from the manipulator on the AUV, the thrusts of the latter's thrusters create the necessary pull at the manipulator's attachment point, which allows it to exert the desired force effects on the object surface. To compensate for the inevitable AUV stabilization system errors, leading to the tool's deviations from the trajectory, the latter is automatically corrected, taking into account the actual AUV deviations. As a result, contact manipulation operations are performed while maintaining the continuous contact of the tool with the object, even with slight displacements of the AUV from the stabilization point. The operability and efficiency of the synthesized system are confirmed by the results of numerical modeling, with the use of basin experimental data and visualization. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicle (AUV) have become an important tool for humans to explore the ocean. The evolution of AUV is trending towards clustering. At present, research on the intelligent control strategy of AUV mostly focuses on the path following and trajectory tracking. Meanwhile, few studies concentrate on the control of an AUV in emergency situations. In the scenario of network attack, an emergency surfacing is necessary. In case of emergency, the loss of AUV can be reduced by timely and stable surfacing. The superiority of reinforcement learning (RL) in AUV control has been proven by many studies. In this work, we used an improved deep reinforcement learning (DRL) method based on deep deterministic policy gradient (DDPG) to solve the problem of AUV surfacing control in emergency situations. The method introduces expert experience data for pre-training and changes the update mechanism of the actor network, thereby improving the convergence rate and stability of the algorithm. Furthermore, we simulate the AUV surfacing control in five typical emergency situations, including partial thruster damage and rudder-jamming. Experimental results indicate that our method enables AUV to choose shorter paths in emergency situations, surfacing to the target area. Moreover, the method converges and maintains a stable reward function after training up to 900 episodes, showing faster convergence and stable performance. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) acquire large-scale multivariate time series (MTS) data during navigation, which can be utilised to realise fault diagnosis, condition monitoring, and other functions by means of classifying the monitoring data. However, due to the complexity and time-variation of relationships between many variables of the MTS, we propose a MTS classification method, namely hybrid feature adaptive fusion network (HFAF). Specifically, a multi-scale method is first proposed to generate monitoring windows with different scales, and the spatiotemporal information is then fully obtained by dilated convolutional neural network (D-CNN) and dilated recurrent neural network (D-RNN). Subsequently, an adaptive feature fusion network based on an attention mechanism is introduced to address the redundancy and conflict between different scales. Finally, the hybrid feature network and adaptive fusion network are stacked up to form HFAF. The effectiveness and superiority of HFAF in AUV fault detection are demonstrated by the experiments conducted on Haizhe AUV, which yields more than 96% precision and more than 95% recall for various faults, outperforming other fault detection methods. [ABSTRACT FROM AUTHOR]

The magnetic coupling wireless charging system may experience changes in mutual inductance and load due to the impact of actual underwater environment, which would lower the system’s transmission efficiency. In response to the circumstance, firstly, this paper proposes the circuit structure of the dynamic autonomous underwater vehicle wireless charging system with LCC-S-Buck-Boost, and a dynamic parameter identification method based on the multi-strategy nonlinear Rime algorithm. Secondly, it is proposed that the difference between the actual input current value of the primary side of the system and the current value calculated by the identification method, and the difference between the actual coil current value of the secondary side of the system and the coil current value calculated by the identification method are used as the adaptability function of the identification method, and the construction of the actual model of the system and the identification algorithm has been completed, and the results of the identification of the system’s mutual inductance and the equivalent load have been obtained. Finally, based on the identification results, it is confirmed that the proposed identification method is efficient and accurate in identifying system mutual inductance and equivalent loads in complex underwater environments by comparing with other algorithms under different values of mutual inductance and equivalent loads.