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13,828 result(s) for "Underwater vehicles"
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A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions
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.
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\"Two lovable mismatched rovers are back for another adventure, this time on an oceanic planet! Organized, careful Rover and impulsive, excitable Speck are ready to set off on their next dangerous, hazardous, mysterious mission: a trip to Unexplored Planet K2-18b. Adventure is underway as they splash down onto the watery surface of this world and get to know the locals. Before long, the two rovers find themselves on a new mission: to help two Spikey-Spikes recover their missing brother from inside the belly of the terrifying Blubber Beast! Will Rover and Speck be able to outwit the Beast and save their new friend? Or will they get -- gulp -- swallowed up too? Jonathan Roth's hugely appealing graphic novel combines classic comic-style art with a gripping plot, loads of extra-silly laughs, and two endearing characters to root for. It's also a great introduction to real rovers and robots, and the entertaining science sidebars sprinkled throughout the story give readers a chance to learn about marine biology, ocean creatures, and more. Backmatter includes a drawing lesson ready-made for classrooms and a fun spot-the-differences activity. This winning combination of accessible STEM content, perfectly kid-friendly humor and loads of adventure is sure to satisfy armchair astronauts everywhere.\"--Provided by publisher
AUV Trajectory Tracking Models and Control Strategies: A Review
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.
A review of different designs and control models of remotely operated underwater vehicle
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.
UUV-Assisted Icebreaking Application in Polar Environments Using GA-SPSO
This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV’s flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions.
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter
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.
Aerial and underwater drones for marine litter monitoring in shallow coastal waters: factors influencing item detection and cost-efficiency
Although marine litter monitoring has increased over the years, the pollution of coastal waters is still understudied and there is a need for spatial and temporal data. Aerial (UAV) and underwater (ROV) drones have demonstrated their potential as monitoring tools at coastal sites; however, suitable conditions for use and cost-efficiency of the methods still need attention. This study tested UAVs and ROVs for the monitoring of floating, submerged, and seafloor items using artificial plastic plates and assessed the influence of water conditions (water transparency, color, depth, bottom substrate), item characteristics (color and size), and method settings (flight/dive height) on detection accuracy. A cost-efficiency analysis suggests that both UAV and ROV methods lie within the same cost and efficiency category as current on-boat observation and scuba diving methods and shall be considered for further testing in real scenarios for official marine litter monitoring methods.
AUV-Based Side-Scan Sonar Real-Time Method for Underwater-Target Detection
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.
A Survey on Visual Navigation and Positioning for Autonomous UUVs
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.