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Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error. [ABSTRACT FROM AUTHOR]

A fuzzy radial basis function neural network (Fuzzy RBFNN) adaptive control scheme, based on fixed-time high gain state observer (FTHGO), is proposed to address the unpredictability of real-time state and composite interference in the trajectory tracking of the fully driven Autonomous Underwater Vehicle (AUV), ensuring fixed-time system convergence regardless of initial conditions. Firstly, a fixed-time backstepping controller is designed and a first-order fixed-time filter is introduced to tackle the differential explosion issue. Secondly, an FTHGO is developed to observe the real-time states of the AUV without assuming global known state signal. Then, the composite interference in the AUV system is effectively compensated by integrating the Fuzzy RBFNN technique. Finally, the fixed-time stability of the entire closed-loop system is proven utilising the Lyapunov stability theory. The effectiveness of the proposed algorithm is proved by the simulation experiment. [ABSTRACT FROM AUTHOR]

To enhance docking safety, the collision dynamics during autonomous underwater vehicle (AUV) docking is investigated. Various factors such as dock structures, motion conditions, and related parameters are considered. The relationship between docking collision force and key parameters is unveiled by the mathematical model based on the restoration coefficient method and equivalent spring damping method. Multivariate nonlinear fitting functions are proposed to evaluate collision force and docking time. Furthermore, the deductions made in the research are supported by a preliminary test and the groundwork for further analysis is laid. The study demonstrates the impacts of various factors on docking, including different docking cone hood profiles, structures, velocities, accelerations, eccentricities, traction forces, angles, and force indices. By clarifying relevant mechanism, docking parameters can be achieved to meet diverse docking requirements and enhance the device's lifespan. The research offers valuable insights for achieving smoother AUV docking with conical hood type docks. [ABSTRACT FROM AUTHOR]

This study explores advanced methods for underwater visual surveys in the photic zone using low-cost autonomous underwater and surface vehicles to enhance marine ecosystem monitoring and analysis. It addresses the challenges of underwater photogrammetry, including data post-processing and analysis, by introducing geo-reference estimation techniques for autonomous-under-vehicle-mounted cameras. Through a novel validation method based on trajectory overlaps and application to real-world datasets, the research demonstrates the effectiveness of these approaches in complex underwater environments. The findings contribute to improving the accuracy of three-dimensional reconstructions of the seafloor, offering significant implications for marine conservation and ecosystem management. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning models have the advantage of being able to control nonlinear systems in an end-to-end manner. However, reinforcement learning controllers trained in simulation environments often perform poorly with real robots and are unable to cope with situations where the dynamics of the controlled object change. In this paper, we propose a DRL control algorithm that combines progressive networks and context as a depth tracking controller for AUVs. Firstly, an embedding network that maps interaction history sequence data onto latent variables is connected to the input of the policy network, and the context generated by the network gives the DRL agent the ability to adapt to the environment online. Then, the model can be rapidly adapted to a new dynamic environment, which was represented by the presence of generalized force disturbances and changes in the mass of the AUV, through a two-stage training mechanism based on progressive neural networks. The results showed that the proposed algorithm was able to improve the robustness of the controller to environmental disturbances and achieve fast adaptation when there were differences in the dynamics. [ABSTRACT FROM AUTHOR]

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations. [ABSTRACT FROM AUTHOR]

Existing autonomous underwater vehicle (AUV) path planning algorithms are rapidly developing and perform well in solving optimal paths. However, the performance of these algorithms in real environments is significantly worse than that in simulated environments due to the influence of currents in real marine environments. To this end, this paper proposes an algorithm that improves the fusion of perturbed flow field and deep reinforcement learning and adds the influence of random currents to the environment, which further improves the overall accuracy of AUV obstacle avoidance in dynamic environments and enhances the AUV's adaptability to the real environment. This study also compares the results obtained using four fused deep reinforcement learning algorithms simulated in different scenarios, and the results show that the proposed algorithm can enable AUV to realize dynamic path planning in unknown environments. [ABSTRACT FROM AUTHOR]

Communication with the underwater vehicles during their tasks is one of the most important issues. The need for real-time data transfer raises the necessity of developing communication systems. Conventional underwater communication systems, such as acoustic systems, cannot satisfy applications that need a high transmission data rate. In this study, we investigate the radio frequency communication system in seawater, which is crucial for real-time data transfer with underwater vehicles. The experiments were in a water tank full of seawater and a real environment in the ocean. Three types of antennae were used: loop antenna, wire antenna, and helical antenna. An Autonomous Underwater Vehicle (AUV) is used as a transmitter to measure the transmission rate as a function of distance. The helical antenna showed better performance regarding the coverage area. Furthermore, the AUV could move freely within the helical and capture live video streaming successfully. This investigation underscores the potential of radio frequency communication systems for enhancing underwater vehicle operations, offering promising avenues for future research and practical implementation. [ABSTRACT FROM AUTHOR]

How to improve the success rate of autonomous underwater vehicle (AUV) path planning and reduce travel time as much as possible is a very challenging and crucial problemin the practical applications of AUV in the complex ocean current environment. Traditional reinforcement learning algorithms lack exploration of the environment, and the strategies learned by the agentmay not generalize well to other different environments. To address these challenges, we propose a novel AUV path planning algorithmnamed the Noisy Dueling Double Deep Q-Network (ND3QN) algorithm by modifying the reward function and introducing a noisy network, which generalizes the traditional D3QN algorithm. Compared with the classical algorithm [e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN], with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path planning, shorter travel time, and smoother paths. [ABSTRACT FROM AUTHOR]

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a "leader–follower" framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated. [ABSTRACT FROM AUTHOR]

The autonomous underwater vehicle (AUV) frequently operates in harsh underwater environments, and timely fault diagnosis of the AUV can prevent mission failure and equipment loss. Data‐driven methods based on a single data source have been widely utilized for fault diagnosis of the AUV because they do not require the construction of complex mechanism models and have high fault diagnosis accuracy. However, these methods face challenges in accomplishing complex fault diagnosis tasks because the single data source provides very restricted fault features. To address this issue, an attention mechanism‐based multisensor data fusion neural network (MDFNN) for AUV fault diagnosis is proposed in this work. First, a feature extraction layer based on the two‐dimensional (2D) convolutional method with a 1D kernel is introduced to extract features from each sensor data separately, significantly optimizing the model architecture. Second, an efficient channel attention mechanism‐based feature fusion layer is proposed to reassign weights to the features of each sensor data, enabling the model to focus more on crucial features. Finally, the fused features are input to the fully connected layers and softmax layer to realize the fault diagnosis of multisensor data. In the end, the diagnostic performance of the proposed MDFNN is evaluated utilizing real AUV experimental data. The experiment shows that the proposed MDFNN has a very fast convergence speed and 98.37% fault diagnosis accuracy, demonstrating its excellent fault diagnosis performance. The proposed MDFNN provides a generalized and simply structured fault diagnosis framework for the AUV with multiple types of sensor data, providing significant engineering value. [ABSTRACT FROM AUTHOR]

Underwater gliders are autonomous underwater vehicles that are widely used in oceanography and coastal surveillance due to their low manufacturing costs and long operation time. This paper addresses the development of a dynamical model of such vehicles which are inertia propelled. The dynamical model is based upon the Boltzmann-Hamel equations modified to variable mass and inertia systems. It yields dynamics in a body-fixed frame using non-inertial coordinates. The theoretical development of the vehicle dynamics based upon the modified Boltzmann-Hamel equations is validated by the longitudinal dynamics model of the underwater glider and its performance resulted from the mass change. [ABSTRACT FROM AUTHOR]

This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages—pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model. [ABSTRACT FROM AUTHOR]

Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error.

Alongside the advancement of navigation, control, and communication technologies, which are fundamental aspects of unmanned underwater vehicles (UUVs), research & development on UUV energy system for long-term mission capabilities has been conducted actively since the 2000s. The hydrogen fuel cell energy system (FCES), which is characterized by low noise, air-independent propulsion, and high energy density, has attracted considerable attention as the energy system for UUVs. In this paper, the key features of the components of a hydrogen FCES meant for use in large-class UUVs are presented. In addition, the current technological development status of FCES-powered large-class UUVs is outlined. Finally, the factors that must be considered when designing large-class UUVs powered by hydrogen FCESs are discussed. • Hydrogen fuel cell systems offer high energy density for long-endurance unmanned underwater vehicles. • Differences between land-based and underwater FCESs include materials and byproduct handling. • Effective design must consider gravimetric and volumetric energy density, heat, and water management. [ABSTRACT FROM AUTHOR]

Traditional wet plug charging for autonomous underwater vehicles (AUVs) limits its application. Inductive power transfer (IPT) is an effective solution to this problem. This paper proposes an arc‐shaped magnetic coupler incorporated with solenoid coils to achieve a stable and efficient output against rotational misalignment for charging AUV. The novel magnetic coupler consists of two solenoid coils and an arc‐shaped coil with a reverse winding, guaranteeing a relatively constant total mutual inductance and decoupling from each other. This magnetic coupler has the characteristics of a compact structure, an ultra‐wide coupling range, and a relatively stable equivalent mutual inductance. The experimental IPT prototype can deliver 1.29 kW with a dc–dc efficiency of 93% under the fully aligned case and 1.2 kW with a dc–dc efficiency of 91% under the misaligned case. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance. [ABSTRACT FROM AUTHOR]

Underwater terrain-aided navigation (TAN) can obtain high-precision positioning independently and autonomously under the conditions of a communication rejection space, which is an important breakthrough for the autonomous and refined operation of deep-sea autonomous underwater vehicles near the seabed. Although TAN originated in the aviation field, the particularity of the underwater physical environment has led to the formation of a different theoretical and technical system. In this article, the application background, operating principles, and most important technical aspects of underwater TAN are introduced. Then, the relevant algorithms involved in the two main modules (the terrain-aided positioning module and the iterative filtering estimation module) of the underwater TAN are reviewed. Finally, other cutting-edge issues in the field of underwater TAN are summarized. The purpose of this article is to provide researchers with a comprehensive understanding of the current research status and possible future developments in the TAN field. [ABSTRACT FROM AUTHOR]

This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model's accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems. [ABSTRACT FROM AUTHOR]

This article discusses the challenges of quantifying changes in marine communities on shallow and mesophotic coral reefs. The authors highlight the difficulty of obtaining high-quality video imagery in mesophotic habitats due to changing topography with depth. They discuss the use of traditional techniques such as visual surveys and photoquadrats, as well as the increasing use of remote or autonomous underwater vehicles (ROV/AUV) for acquiring imagery. The authors emphasize the importance of orthorectified imagery, obtained through a process of orthorectification, to minimize errors in quantitative analyses of benthic taxa. They respond to criticisms and clarify their use of area-based metrics and the term "orthorectified." Overall, the article emphasizes the need for proper post-processing and pre-planning when using imagery for quantitative analysis of marine communities. Additionally, the authors criticize the quality of the images and subsequent data presented in Scott et al. (2019) due to the use of compromised images, and address criticisms made by Pawlik (2019) regarding their previous work. They defend the peer review process of their manuscript and conclude that the comments made by Pawlik do not undermine their approach to re-analyzing the ROV data. [Extracted from the article]

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities. [ABSTRACT FROM AUTHOR]

Underwater vehicles heavily depend on the integration of inertial navigation with Doppler Velocity Log (DVL) for fusion-based localization. Given the constraints imposed by sensor costs, ensuring the optimization ability and robustness of fusion algorithms is of paramount importance. While filtering-based techniques such as Extended Kalman Filter (EKF) offer mature solutions to nonlinear problems, their reliance on linearization approximation may compromise final accuracy. Recently, Invariant EKF (IEKF) methods based on the concept of smooth manifolds have emerged to address this limitation. However, the optimization by matrix Lie groups must satisfy the "group affine" property to ensure state independence, which constrains the applicability of IEKF to high-precision positioning of underwater multi-sensor fusion. In this study, an alternative state-independent underwater fusion invariant filtering approach based on a two-frame group utilizing DVL, Inertial Measurement Unit (IMU), and Earth-Centered Earth-Fixed (ECEF) configuration is proposed. This methodology circumvents the necessity for group affine in the presence of biases. We account for inertial biases and DVL pole-arm effects, achieving convergence in an imperfect IEKF by either fixed observation or body observation information. Through simulations and real datasets that are time-synchronized, we demonstrate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) have been widely used in ocean missions. When they fail in the ocean, it is important to predict their trajectory. Existing methods rely heavily on historical trajectory data while overlooking the influence of the ocean environment on an AUV's trajectory. At the same time, these methods fail to use the dependency between variables in the trajectory. To address these challenges, this paper proposes an AUV trajectory prediction model known as the nonlinear Kepler optimization algorithm–bidirectional long short-term memory–time-variable attention (NKOA-BiLSTM-TVA) model. This paper introduces opposition-based learning during the initialization process of the KOA and improves the algorithm by incorporating a nonlinear factor into the planet position update process. We designed an attention mechanism layer that spans both time and variable dimensions, called TVA. TVA can extract features from both the time and variable dimensions of the trajectory and use the dependency between trajectory variables to predict the trajectory. First, the model uses a convolutional neural network (CNN) to extract spatial features from the trajectory. Next, it combines a BiLSTM network with TVA to predict the AUV's trajectory. Finally, the improved NKOA is used to optimize the model's hyperparameters. Experimental results show that the NKOA-BiLSTM-TVA model has an excellent parameter optimization effect and higher prediction accuracy in AUV trajectory prediction tasks. It also achieves excellent results in ship trajectory prediction. [ABSTRACT FROM AUTHOR]

In base-station-based underwater wireless acoustic networks (B-UWANs), effective handover mechanisms are necessary to ensure seamless data services for mobile nodes such as autonomous underwater vehicles (AUVs). Unlike terrestrial base stations (BSs), moored buoy BSs in B-UWANs experience motion responses due to wave loads under environmental conditions, posing unique challenges to the handover process. This study examines how BS motion affects handover decision errors, which arise when AUVs incorrectly initiate handovers to unintended BSs due to BS motion. By utilizing the AUV–BS distance as a handover triggering parameter, our analysis reveals a significant increase in decision errors within the overlapping regions when both the current and target BSs are in motion, especially when moving in the same direction. In addition, these errors intensify with the magnitude of BS motion and are exacerbated by smaller BS network radii. Based on these simulation results, we present an analytical framework that not only measures the influence of BS motion on the AUV–BS distance but also provides strategic insights for refining underwater handover protocols, thereby enhancing operational reliability and service continuity in B-UWANs. [ABSTRACT FROM AUTHOR]

This research addresses the design of an Adaptive Fuzzy Sliding-Mode Control (AFSMC) for a group of nonlinear under-actuated systems with unknown input gain functions. In the proposed approach, to provide a structure for controlling the under-actuated subsystem, the sliding manifold corresponding to the whole dynamic system is derived based on the backstepping technique. In addition, two separate fuzzy inference systems are utilized for approximating the system's internal dynamics and the unknown input gain function. In order to derive the robust part of the controller, a novel method is proposed to define the upper bound of the uncertain term in the form of a state-dependent second-degree polynomial. It should be mentioned that the output vectors of the fuzzy systems and the coefficients of the robust part of the controller are obtained by the designed adaptation laws. The asymptotic stability analysis for the proposed AFSMC structure is provided by the second theorem of Lyapunov and Barbalat's lemma. In the end, the AFSMC effectuality is confirmed by the depth control of a REMUS Autonomous Underwater Vehicle (AUV). [ABSTRACT FROM AUTHOR]

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

In this paper, we investigate the concept of polymorphism in the context of artificial swarms; that is, collectives of autonomous platforms such as, for example, unmanned aerial systems. This article provides the reader with two practical insights: (a) a proof-of-concept simulation study to show that there is a clear benefit to be gained from considering polymorphic artificial swarms; and (b) a discussion on the design of user-friendly human–machine interfaces for swarm control to enable the human operator to harness these benefits.

This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance.

A comprehensive review of Autonomous Underwater Gliders encompasses their development, technological advancements, operational principles, and applications in various fields. It explores the different types of architectures, such as those with blended wing or conventional designs, and examines their roles in scientific research and civil use. The review also addresses the challenges and limitations in areas like payload, navigation, swarm management, and the effects of underwater environments on glider performance. This knowledge is essential for improving glider technology and expanding their potential in future underwater exploration and data collection missions.