studies-rov-concepts_2024-now

Studies of ROV concepts 2024 to now

Authors: Guangyuan Liu, Nguyen Van Huynh, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Kun Zhu, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, and Dong In Kim Advances in AI and robotics have increased interest in swarms of unmanned vehicles for tasks that are too risky for humans. However, managing and coordinating these swarms in dynamic settings poses significant challenges for traditional AI methods. Generative AI (GAI) could be a game-changer here, given its ability to handle complex data. This paper surveys the applications, challenges, and opportunities of GAI in unmanned vehicle swarms.

Authors: Aryan Anand, M. Yuva Bharath, Prabha Sundaravadivel, J. Preetha Roselyn, and R. Annie Uthra This study introduces and describes a new approach to underwater exploration that integrates Artificial Intelligence (AI) with Autonomous Underwater Vehicles (AUVs). It highlights the benefits of combining AI with biomimicry to enhance the capabilities of AUVs, enabling them to emulate the efficient and graceful movements of marine creatures. The text outlines the development of a new propulsion system inspired by marine organisms, specifically cuttlefish, and discusses the advantages of this system in terms of energy efficiency and reduced environmental impact.

11 - Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Authors: Fomekong Fomekong Rachel Merveille, Baozhu Jia,and Zhizun Xu This presentation discusses advancements in underwater navigation for Unmanned Underwater Vehicles (UUVs) by integrating deep learning methodologies and sensor technologies. It highlights the challenges faced by traditional navigation methods due to signal loss in water and proposes the use of artificial intelligence, specifically deep learning and visual Simultaneous Localization and Mapping (SLAM), to enhance navigation accuracy and reliability. The authors also provide detailed insights into various sensor technologies and their applications in underwater navigation.

Author: Changho Yun This paper advocates for the Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as an effective channel access protocol for distributed underwater cognitive acoustic networks (UCANs) by explaining the advantages of UMMAC-CA, such as improved spectral efficiency, reduced channel access overhead, and better performance metrics compared to the existing Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR).

Author: Wenwei Zhang, Kun Zhu, Zhichun Yang, Yunling Ye, Junfeng Ding, and Jin Gan This study addresses the challenges of detecting underwater damage to structures with pile foundations. It introduces the design and evaluation of an adsorption- operated robotic system equipped with an automatic movement mechanism and a force-redeemed active disturbance rejection controller. Additionally, it develops a new detection algorithm based on the image segmentation network UNet.

Author: Angelo Mari C. Paredes, & Edwin R. Arboleda This study analyzes the challenges and advancements in underwater communication for Underwater Wireless Sensor Networks (UWSNs) by highlighting the difficulties posed by the underwater environment, such as attenuation, multipath propagation, and limited bandwidth, and discusses how innovative antenna designs can address these issues. It examines different types of antennas (monopole, dipole, and helical), their trade-offs, and advancements in materials and reconfigurable antennas that offer promising solutions.

Authors: Shuangquan Li, Zhichen Zhang, Qixian Zhang, Haiyang Yao, Xudong Li, Jianjun Mi, & Haiyan Wang This document reviews and analyzes recent advancements in underwater optics, particularly focusing on the challenges and developments in underwater optical transmission. It highlights the importance of understanding underwater optical laws and physical models and their applications in various domains, such as underwater resource exploration, autonomous underwater vehicle navigation, and underwater wireless optical communication. It also identifies future directions and emphasizes the need for further research to enhance underwater optical technologies and their applications.

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Authors: Pei Li, Zongyao Li, Chaoyang Chen, Juan Chen, and Zuguo Chen This article explains a novel method for improving the positioning accuracy of autonomous underwater vehicles (AUVs) by addressing communication delays, describing how it uses relative angle correction and Doppler measurement data integrated into an Extended Kalman Filter (EKF) to reconstruct measurement information and reduce positioning errors. The effectiveness of this method is demonstrated through a simulation study, showing significant improvements in localization accuracy.

02 - Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers

Authors: Jia Wang, Tianyi Tao, Daohua Lu, Zhibin Wang, and Rongtao Wang This document presents a new algorithm designed to enhance the efficiency of Autonomous Underwater Vehicles (AUVs) by addressing task allocation and path planning challenges to highlight the limitations of current AUV energy usage and deployment methods, introduce the proposed algorithm, and demonstrate its effectiveness through simulation and comparative experiments. The system's ultimate goal is to improve the operational efficiency of heterogeneous AUV clusters by optimizing task execution and energy management.

Authors: Liwei Zhi and Yi Zuo This proposal addresses the path planning problem for multiple Autonomous Underwater Vehicles (AUVs) using a novel method called adaptive multi-population particle swarm optimization (AMP-PSO). It explains the methodology implemented, including the grouping strategy and exchanging mechanism of particles, and highlights the effectiveness of AMP-PSO through simulation results.

Authors: Jimin Hwang, Neil Bose, Gina Millar, Craig Bulger, Ginelle Nazareth, and Xi Chen This paper describes the objectives, methodology, and results of a research study focused on testing an adaptive sampling method for an autonomous underwater vehicle (AUV) to track a hydrocarbon plume in the water column and validate the system through field trials. It outlines the components of the system, the testing process, and the successful outcomes, emphasizing the system's resilience and applicability in marine pollutant assessment and mitigation.

Authors: Lin Zhang, Yanbin Gao, and Lianwu Guan This paper highlights the limitations of traditional navigation systems like strapdown inertial navigation systems (SINS) and Doppler velocity logs (DVL) due to positioning errors and susceptibility to failure in complex underwater conditions. It proposes an integrated navigation approach using factor graph optimization (FGO) and an improved pre-integration technique that incorporates side-scan sonar (SSS) derived position measurements to demonstrate how this new method improves the reliability and accuracy of navigation in AUVs, particularly in challenging environments, and to share the positive results from marine experiments.

Authors: Zheping Yan, Mingyao Zhang, Jiajia Zhou and Lidong Yue This paper introduces a novel control approach, the Distributed Lyapunov-based Model Predictive Controller (DLMPC) with a Fast Finite-Time Extended State Observer (FFTESO), to enhance the performance of AUVs by compensating for external disturbances and internal uncertainties. It explains the methodology implemented, including developing position and velocity tracking controllers, managing system constraints, and applying a Lyapunov-based backstepping control law to ensure stability. It also highlights the effectiveness of the proposed method through simulation results, demonstrating improved convergence speed and tracking accuracy.

Authors: Jacek Zalewski, and Stanisław Hozyn This document highlights the current problem of inadequate navigation in AUVs due to weaknesses in electronic equipment and the limitations of traditional radio navigation systems. It introduces a novel approach that involves generating a visual representation of the vehicle's surroundings during temporary surfacing and comparing it with a map's shoreline representation to enhance positioning accuracy. The method is particularly aimed at low-cost AUVs lacking advanced navigation systems, and the text suggests that further research will explore its application in fully autonomous navigation systems.

Authors: Kene Li, Liuying Li, Chunyi Tang, Wanning Lu, and Xiangsuo Fan This paper outlines a six-direction search scheme based on neural networks, detailing how it constructs obstacle energy and path energy to optimize path planning. It also discusses two optimization methods to improve the efficiency of the search algorithm, aiming to reduce iterations and search time. It concludes by stating that simulation results validate the effectiveness and efficiency of the proposed scheme.

Author: Shuo Pang, Ye Li, Liang Xiao, Francisco Rego, and Teng Ma This paper aims to showcase the benefits and future implications of using unmanned surface and underwater vehicles in extreme environments for monitoring and safeguarding ocean resources. It summarizes key research papers that address challenges and innovations in areas such as sensing, control, navigation, and communication of these vehicles, providing insights into their operation and potential for enhancing oceanographic data collection.

Author: Hong Zhu, Lunyang Lin, Chunliang Yu, Yuxiang Chen , Hong Xiong, Yiyang Xing, and Guodong Zheng This paper presents a study focusing on enhancing the maneuverability of autonomous underwater vehicles (AUVs) through the development of a novel vectored thruster and optimized blade design, detailing the design specifications, testing, and performance evaluation of the thruster and highlighting its effectiveness in improving AUV control and efficiency in both controlled and offshore environments.

Author: Pinchi Li, Xiaona Sun, Ziyun Chen, Xiaolin Zhang, Tianhong Yan, and Bo He This study addresses the challenges posed by outliers and non-Gaussian noise in underwater environments, which can affect the performance of navigation algorithms. The proposed solution combines the Maximum Correntropy Criterion (MCC) with the Variational Bayesian approach to enhance robustness and adaptability. It details the methodology, including using an Unscented Kalman Filter (UKF) and evaluating the algorithm's performance through simulations and sea trials, highlighting significant improvements in navigation accuracy.

Author: Huibao Yang, Xiujing Gao, Bangshuai Li, Bo Xiao, and Hongwu Huang This article describes the development and optimization of the Chan-Taylor algorithm, specifically introducing the Weighted Modified Chan-Taylor (WMChan-Taylor) algorithm and an error-corrected version of it. It also explains how these algorithms improve positioning accuracy by correcting noise variance and isolating noisier stations, supported by computer and semi-physical simulation experiments.

17 - Robust Prescribed-Time ESO-Based Practical Predefined-Time SMC for Benthic AUV Trajectory-Tracking Control with Uncertainties and Environment Disturbance.

Author: Yufei Xu, Ziyang Zhang, and Lei Wan This study focuses on developing a control method for benthic autonomous underwater vehicles (AUVs) that addresses challenges related to model uncertainties and external disturbances. It introduces a robust prescribed- time extended state observer (RPTESO) and a non-singular robust practical predefined-time sliding mode control (RPPSMC) to enhance control accuracy and robustness. It also conveys these methods' design, implementation, and effectiveness through theoretical analysis and simulations.

Author: Chenghao Zhang, Jing Zhang, Jiafu Zhao, and Tianchi Zhang This study highlights the importance of accurate drift trajectory predictions for disaster response and navigational safety and explains how the proposed model leverages historical drift patterns and ocean currents to enhance prediction accuracy. It also details the model's features, such as the double-branch adaptive span attention mechanism, and présents evaluation results showing its improved performance compared to other methods.

Author: Huanyu Ou, Yuli Hu, Zhaoyong Mao, Wenlong Tian, and Bo Cheng This study proposes a cooling improvement method using heat bridges (HBs) for the shell-mounted propulsion motor (SmPM) and details the analysis, development, testing, and optimization of this method. The presentation aims to convey the effectiveness of the proposed cooling solution in reducing the motor's maximum winding temperature and weight, thereby enhancing the motor's reliability and stability.

Author: Zhuoyu Zhang, Wangjie Ding, Rundong Wu, Mingwei Lin, Dejun Li, and Ri Lin This paper introduces a vision guidance algorithm and a fusion positioning algorithm to enhance accuracy and effectiveness in these operations, supported by simulation results and experimental validation.

Author: Raqibur Rahman This paper details the training and testing of different depth prediction models and poses prediction models using different datasets and input spaces. It also discusses the use of these predicted depths in underwater image enhancement and RGB-D SLAM pipelines to demonstrate that techniques originally developed for airborne vision can be adapted to underwater environments where traditional depth sensing methods are challenging.

22 - Analysis of the Effect of Base Station Motion on Underwater Handovers for Base-Station-Based Underwater Wireless Acoustic Networks

Author: Changho Yun, and Yong-Ju Kwon This document presents a study on the challenges and solutions related to handover mechanisms in base-station- based underwater wireless acoustic networks (B-UWANs), focusing on how the motion of moored buoy base stations, influenced by environmental conditions, affects handover decision errors for mobile nodes like autonomous underwater vehicles (AUVs). It analyzes these errors and proposes an analytical framework to improve handover protocols, thereby enhancing the reliability and continuity of data services in such networks.

23 - Autonomous Underwater Vehicle Trajectory Prediction with the Nonlinear Kepler Optimization Algorithm–Bidirectional Long Short-Term Memory–Time-Variable Attention Model

Author: Jieen Yao, Junzheng Yang, Chenghao Zhang, Jing Zhang, and Tianchi Zhang This paper highlights the limitations of existing methods and presents the proposed model, NKOA-BiLSTM-TVA, as a solution that incorporates various advanced techniques such as opposition-based learning, a nonlinear Kepler optimization algorithm, and a time-variable attention mechanism. The intent is to demonstrate the effectiveness and accuracy of this model in predicting AUV trajectories, as well as its applicability to ship trajectory prediction.

27 - Development of a Control System for Underwater Vehicles with Multilink Manipulators Performing Contact Manipulation Operations

Authors: Alexander Konoplin, Nikita Krasavin, Alexander Yurmanov, Pavel Piatavin, Roman Vasilenko, and Maxim Panchuk This paper describes a new method for controlling autonomous underwater vehicles (AUVs) equipped with multilink manipulators, enabling AUVs to perform contact manipulation operations in a stabilized hovering mode near or above target objects. It outlines the process of calculating the force vector needed for the manipulator's tool to exert the desired force on an object's surface, generating control signals for additional movements, and adjusting the AUV's thrusters to maintain the necessary force. It also discusses compensating for stabilization errors to ensure continuous contact with the object. The effectiveness of this system is supported by numerical modeling and experimental data.

Authors: Can Wang, Chensheng Cheng, Chun Cao, Xinyu Guo, Guang Pan and Feihu Zhang This document discusses the limitations of existing methods, such as the Extended Kalman Filter (EKF) and Invariant EKF (IEKF), and proposes an alternative fusion filtering approach that addresses these limitations to highlight the innovation and effectiveness of this new methodology through simulations and real datasets, emphasizing its robustness and optimization capabilities in the context of underwater multi-sensor fusion.

Authors: Yueming Li, Mingquan Ma, Jian Cao, Guobin Luo, Depeng Wang and Weiqiang Chen. This document outlines AUVs' challenges in unknown and uncertain underwater environments, such as limited detection capabilities and the inability to obtain real-time global state information. It explains how multi-agent reinforcement learning (MARL) is used to improve autonomous decision-making and collaboration among AUVs by modeling the search task as a decentralized partial observation decision process and establishing a multi-AUV cooperative area search system (MACASS), allowing AUVs to perform more efficiently in both civilian and military applications.

Authors: Xiaokai Mu, Yuanhang Yi, Zhongben Zhu, Lili Zhu, Zhuo Wang, and Hongde Qin This study aims to improve speed estimation accuracy by utilizing a Self-Attention mechanism with Long Short-Term Memory (LSTM) to process various input variables. It also seeks to enhance the model's generalization capability by incorporating water-track velocity information. It highlights the effectiveness of this model in comparison to traditional methods, particularly in scenarios where persistent bottom-track velocity failures occur, as demonstrated by sea trial experiments.

Authors: Mohammad Afi f Kasno, Izzat Nadzmi Yahaya, and Jin- Woo Jung This study highlights the challenges faced by ROV operators due to the reliance on 2D camera feeds, which affect orientation awareness and accuracy. It presents a solution using nine-degrees-of-freedom sensors to capture real-time orientation data, which is then used to create a 3D model for better visualisation. It also mentions the constraints of testing due to operator agreements and presents the effectiveness of the system, noting a low mean absolute error, indicating its potential as a cost- effective solution.

Authors: Patrick Ng, and Michael Krieg This paper intends to describe the improvements made to the ArduSub system for the BlueROV2 Heavy, focusing on enhancing simulation accuracy and autonomous controller design. It details the implementation and validation of these improvements, including updates to the simulation model, replacement of the manual control algorithm, and the development of a proportional–derivative (PD) controller within a Robot Operating System (ROS) to aid augmented reality pilots, also outlining the testing processes and results.

Authors: Guangwu Song , Wei Chen, Qilong Zhou and Chenkai Guo This document describes a proposed enhancement to underwater target identification techniques using an improved version of the YOLOv8 model. It explains the modifications made to the model, such as using deformable convolution and replacing the loss function with another to improve identification accuracy and picture quality in underwater environments. This document also highlights the improved performance metrics achieved by the enhanced algorithm, demonstrating its effectiveness for the real-time detection needs of underwater robots.

Authors: Hailin Wang, Yiping Li, Shuo Li, and Gaopeng Xu This study addresses optimizing task allocation for multiple Autonomous Underwater Vehicles (AUVs) under energy constraints by conceptualizing it as a Capacitated Vehicle Routing Problem (CVRP) and using the Solving Constraint Integer Programs to find effective strategies. It highlights the significance of this research for marine exploration, environmental monitoring, and underwater construction and emphasizes the theoretical and practical contributions of the study in improving operational efficiency and mission duration in energy-restricted underwater environments.

Authors: Xuejiao Yang, Yunxiu Zhang, Rongrong Li, Xinhui Zheng, and Qifeng Zhang This paper describes the limitations of current methods, such as real-time teleoperation and preprogramming. It introduces a new method using learning from demonstrations (LfD) and dynamic movement primitives (DMP) to enhance task reproduction and generalization in complex underwater environments. The proposed underwater DMP (UDMP) method aims to address the challenges posed by the stochastic nature of underwater environments and improve operational efficiency by reducing the cognitive burden on operators. It also mentions using Gaussian mixture models for feature extraction and the transition from homomorphic to heteromorphic teleoperation modes.

36 - Adaptive Control for Underwater Simultaneous Lightwave Information and Power Transfer: A Hierarchical Deep- Reinforcement Approach

Authors: Huicheol Shin, Sangki Jeong, Seungjae Baek, and Yujae Song This study focuses on developing an advanced algorithm for optimizing energy harvesting and communication performance in an underwater optical wireless communication system. That involves a scenario where an underwater sensor communicates with a remotely operated vehicle, and the goal is to maximize energy harvesting at the sensor while maintaining communication standards. The study also outlines a hierarchical deep reinforcement learning approach and deep deterministic policy gradient-based online algorithm to achieve this objective.

Authors: Gang Sun, Shengtao Chen, Hongkun Zhou, and Fei Wan This study analyzes dual floating bodies' interaction dynamics and hydrodynamic coefficients to optimize salvage strategies. It discusses specific parameters and phases of the salvage process, such as the berthing stage, side-by-side phase, and towing phase, to enhance stability and efficiency in these operations.

38 - Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post- Processing Model Based on Factor Graph Optimization

Authors: Lin Zhang, Lianwu Guan, Jianhui Zeng, and Yanbin Gao This study aims to address the challenge of improving the accuracy of navigation data for Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) used in seabed mapping. It introduces a post-processing navigation method using Factor Graph Optimization (FGO) to enhance the accuracy of AUV trajectories by integrating various data sources and validating the method through simulations and experiments.

Authors: Jaime Alonso Restrepo-Carmona, Elkin A. Taborda, Esteban Paniagua-García, Carlos A. Escobar, Julián Sierra- Pérez, and Rafael E. Vásquez This paper presents a research study that integrates Systems Engineering (SE) methodologies with Industry technologies to enhance the design and performance of underwater robotic systems, specifically focusing on Remotely Operated Vehicles (ROVs). It aims to demonstrate how SE can systematically incorporate tools to improve mission performance and meet stakeholder expectations using a case study of an underwater exploration vehicle. It also seeks to address design challenges and provide a structured methodology for advancing the design and functionality of complex systems.

40 - Robust H∞ Control for Autonomous Underwater Vehicle’s Time- Varying Delay Systems under Unknown Random Parameter Uncertainties and Cyber-Attacks

Authors: Soundararajan Vimal Kumar and Jonghoek Kim This study addresses challenges such as time-varying delays, model uncertainties, and cyber-attacks that can affect the performance and stability of AUV systems. It aims to enhance the robustness of these systems by using advanced mathematical techniques, including Lyapunov–Krasovskii functional and linear matrix inequality, to derive new stability criteria. The research is positioned as a novel due to its comprehensive approach to handling multiple complexities simultaneously, and the effectiveness of the proposed solutions is validated through examples and simulations.

Authors: Xinglong Feng, Yuzhong Zhang, Ang Gao, and Qiao Hu This document presents research findings on improving underwater communication for autonomous underwater vehicles (AUVs) by addressing issues such as high attenuation, weak reception signals, and channel blockage. That involves using a multi-node networking communication system based on the CSMA/CA protocol, comparing the performance of OLSR and AODV protocols, and proposing a dynamic backoff method to enhance communication reliability and efficiency.

Authors: Zhiyuan Xu, Yong Shen, Zhexue Xie, and Yihua Liu This paper is a study on developing and enhancing path- planning algorithms for Autonomous Underwater Vehicles (AUVs). It introduces the Field Theory Augmented algorithm, which integrates field theory principles with the classical algorithm to address macro- global and micro-local path planning challenges. It also highlights the improvements in navigation safety and efficiency achieved by this algorithm, demonstrating its practical value for AUV operations in complex marine environments.

Authors: Guanghao Yang, Weidong Liu, Le Li, Jingming Xu, Liwei Guo, and Kang Zhang This paper explains a new approach for 3D trajectory tracking in remotely operated vehicles (ROVs) that combines deep neural networks with event-triggered nonlinear model predictive control. The text aims to highlight the advantages of this double closed-loop control system over traditional single-loop model predictive control, particularly in terms of handling external disturbances, reducing computational burden, and improving tracking performance. Additionally, it seeks to demonstrate the effectiveness of this method through simulation results.

Authors: Yuliang Wang, Han Bao, Yiping Li, and Hongbin Zhang This paper describes a technical approach for improving the control and stability of underactuated Autonomous Underwater Vehicles (AUVs) in the vertical plane that outlines the use of mathematical and control theory methods, such as SO(3) representation, exponential mapping, interval type II fuzzy systems, backstepping, and the small gain theorem, to address challenges like Euler angle singularity, quaternion ambiguity, model changes, ocean currents, and actuator saturation. It aims to convey the effectiveness of the proposed controller through simulation results.

Authors: Fushen Ren and Zhongyang Wang This document outlines the challenges addressed in the robot's design, such as modular structure, motion modes, and control systems, and highlights the use of a sliding mode control algorithm to enhance the robot's stability and efficiency underwater. It also compares this algorithm with a Proportional-Integral-Derivative (PID) control algorithm, emphasizing the advantages of the sliding mode control in terms of response speed and stability..

Authors: Raphaël Garin, Pierre-Jean Bouvet, Beatrice Tomasi, Philippe Forjonel, and Charles Vanwynsberghe This paper discusses a novel low-cost algorithm for simultaneous communication and localization of autonomous underwater vehicles (AUVs). It outlines the challenges of AUV navigation due to the limitations of radio-frequency signals underwater and the absence of GNSS, proposing a solution that involves using acoustic signals to estimate the location of an AUV by analyzing Doppler shift and range data exchanged with a fixed beacon. It also mentions evaluating this algorithm through numerical simulations and at-sea experiments.