Explore the vast range of titles available.

Communication constraint is the main factor that affects networked autonomous underwater vehicles’ (NAUVs) control performance. This article investigates the distributed event-driven formation control for NAUVs undergoing external disturbances and uncertain hydrodynamics. As distributed systems, the achievement of the target geometrical configuration hinges heavily on neighboring communication. However, the risk of traffic jamming will arise when an underwater acoustic communication network is occupied by multiple information packets broadcasted from different vehicles. Worse still, potential packet losses and time delays caused by traffic jamming may destroy the system stability. Regarding this, by introducing a distributed observer for each vehicle, the leader-follower distributed formation control is categorized into a localization layer and a tracking layer. Furthermore, a trade-off between communication constraint and control performance is achieved under this framework. Specifically, a novel interleaved periodic dynamic event-triggered communication mechanism is integrated into the upper distributed observer, endowing NAUVs with anti-competing interaction capability. By resorting to an anti-winding finite-time prescribed performance algorithm in the lower tracking layer, the tracking accuracy is guaranteed even in circumstances of large-angle formation motions. The prominent feature of the proposed control scheme lies in ensuring the formation flexibility and tracking accuracy under unreliable communication network. Simulation results are constructed to illustrate the effectiveness of this work.

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

The limitations of underwater acoustic communications mean that the side-scan sonar data of an autonomous underwater vehicle (AUV) cannot be transmitted back and processed in real time, which means that targets cannot be detected in real time. To address the problem, this paper proposes an autonomous underwater vehicle-based side-scan sonar real-time detection method for underwater targets. First, the paper describes the system and operation of real-time underwater-target detection by the side-scan sonar mounted on the autonomous underwater vehicle. Next, it proposes a real-time processing method for side-scan sonar data, method for constructing a deep-learning-based underwater-target detection model, and real-time method for underwater-target detection based on navigation strip images, which, together, solve the three key technical problems of real-time data processing, deep-learning-based detection model construction, and real-time target detection based on the autonomous underwater vehicle. Finally, through sea-based experiments, the effectiveness of the proposed methods is evaluated, providing a new solution for the autonomous underwater vehicle-based side-scan sonar real-time detection of underwater targets.

Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.

This article reviews remotely operated underwater vehicle (ROUV) and its different types focusing on the control systems. This study offers a brief introduction of unmanned underwater vehicle (UUV) together with ROUV. Underwater robots are designed to work as an alternative to humans because of a difficult and hazardous underwater environment. The applications and demand of marine robots are increasing with the passage of time. There are several research articles and publications available on these topics but, a complete review of old and recent research about this technology is still hard to find. This article also assesses some recently published research papers on underwater systems. It presents the comparison of different control systems and designs of underwater vehicles. There have been major developments in marine technology depending on the needs, applications and cost of different missions. Scientists design many remotely operated vehicles based on the educational or industrial purposes. This article is presented in order to help and assist the future researchers as a massive review of the field of remotely operated underwater vehicles and their possible future developments are presented.

Highly divergent estimates of benthic cover of sponges have been reported for Caribbean mesophotic reefs (90–100 m) based on quadrat point-intercept data collection using 2 methods: visual surveys conducted in situ by technical divers, and analyses of photographs taken by unmanned underwater vehicles (UUVs). The second method has been criticized for potential errors from image distortion caused by variable camera angle relative to the substratum, but without a broader comparison of both methods. We find that studies that have used the UUV-based method are advantageous for a number of reasons, most importantly: (1) access to the full mesophotic zone, (2) higher sample replication, and (3) reduced likelihood of sampling bias. For tech diving surveys conducted at 91 m, i.e. the deepest depth reported using this method but only midway through the mesophotic zone, studies have reported particularly high sponge cover (∼80 vs. <10% for UUV-based surveys), which may be a consequence of low replication and targeted sampling influenced by very short working times under hazardous conditions. When evaluating benthic abundance metrics from photographs, issues associated with variable substratum angle are common to any topographically complex surface, particularly within a quadrat. Nevertheless, point-intercept estimates are not dependent on quadrat area and are not subject to error due to image distortion or surface complexity. Unlike visual census data from tech dives, UUV photographs can be validated by taxonomic experts and archived for re-analysis. Past tech diving surveys should be repeated using the UUV-based method with greater replication over the full range of the mesophotic zone in order to reconcile divergent estimates of benthic cover.

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

This review examines current underwater communication technologies, highlighting the challenges and innovations in applications spanning scientific research, exploration, environmental monitoring, and security. Emphasis is placed on the evolution and interplay of acoustic, optical, quantum, and hybrid communication methods, as well as their respective limitations and potential solutions in the complex underwater environment. The review explores advancements in Autonomous Underwater Vehicles (AUVs), with particular focus on the swarm configuration, which enables dynamic, interconnected networks for real-time data exchange and adaptive responses to environmental changes. Key areas of innovation include the use of new materials, advanced sensor networks, and machine learning algorithms to enhance communication efficiency, security, and resilience under varying underwater conditions. The integration of swarm AUVs and Internet of Things (IoT) concepts is proposed to further expand underwater operational capabilities, making underwater communication systems more reliable, secure, and versatile.

Autonomous localization and navigation, as an essential research area in robotics, has a broad scope of applications in various scenarios. To widen the utilization environment and augment domain expertise, simultaneous localization and mapping (SLAM) in underwater environments has recently become a popular topic for researchers. This paper examines the key SLAM technologies for underwater vehicles and provides an in-depth discussion on the research background, existing methods, challenges, application domains, and future trends of underwater SLAM. It is not only a comprehensive literature review on underwater SLAM, but also a systematic introduction to the theoretical framework of underwater SLAM. The aim of this paper is to assist researchers in gaining a better understanding of the system structure and development status of underwater SLAM, and to provide a feasible approach to tackle the underwater SLAM problem.