Studies of ROV concepts 2024 - Part A
Back to the menu
Next page Next page BUTTON TEXT Previous page 12 - Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
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.
15 - On-Device Intelligence for AI-Enabled Bio-Inspired Autonomous Underwater Vehicles (AUVs)
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.
16 - 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.
19 - Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks
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).
33 - Development of an Underwater Detection Robot for the Structures with Pile Foundation
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.
34 - Antennas for Underwater Wireless Sensor Networks (UWSNs): Reviewing the challenges of underwater communication
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.
39 - Breakthrough underwater physical environment limitations on optical Information representations: An overview and suggestions
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.
Click on the octopus to return to the top of the page
02- A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays
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.
03 - 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.
04 - Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO
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.
07 - Adaptive AUV Mission Control System Tested in the Waters of Baffin Bay
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.
08 - Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration
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.
09 - Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints
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.
10 - Computer Vision-Based Position Estimation for an Autonomous Underwater Vehicle
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.
11 - Three-Dimensional Path Planning Based on Six-Direction Search Scheme
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.
20 - Editorial: Unmanned marine vehicles for ocean observation
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.
23 - The Design and Application of a Vectored Thruster for a Negative Lift-Shaped AUV
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.
24 - A Robust and Adaptive AUV Integrated Navigation Algorithm Based on a Maximum Correntropy Criterion
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.
25 - Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm
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.
26 - 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.
27 - Prediction of Drift Trajectory in the Ocean Using Double-Branch Adaptive Span Attention
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.
28 - Cooling Improvement for High-Power-Density Shell-Mounted Underwater Propulsion Motors with Heat Bridges
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.
29 - Autonomous Underwater Vehicle Cruise Positioning and Docking Guidance Scheme
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.
30 - Self Supervised Scale Consistent Depth and Ego-motion Learning from Monocular Video for underwater robots
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.
31 - 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.
32 - 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.
1 - Obstacles Detection in Underwater Environment Using ROV Based on Convolutional Neural Network
Authors: Purwidi Asri, Yuning Widiarti, Endang Pudji Purwanti, Endah Wismawati, M. Firman Tsany Arifin Detecting obstacles underwater is hard for Remotely Operated Vehicles (ROVs) due to limited visibility. This study develops a better detection system using a Convolutional Neural Network (CNN). It aims to help ROVs avoid obstacles during missions like ship inspections and marine exploration. The system uses an underwater camera and CNN-based image processing, trained on various underwater images, achieving 99. 25% validation accuracy and 85% real-time classification accuracy. This method improves ROV reliability for marine applications.
13 - Implementation of miniaturized power transmission system of ROV
Authors: Mingyu Yang, Shiyu Zhao, Yixuan Tong and Guangyu Lu The power transmission system is essential for ROVs to operate underwater securely. A new method using matrix transformation reduces transmission loss and current. By increasing transmission frequency at the underwater end, transformer size is minimized, enhancing efficiency for high-power ROV applications.
14 - Numerical simulation of hydrodynamics of ocean-observation- used remotely operated vehicle.
Authors: Dapeng Zhang, Bowen Zhao, Yi Zhang, and Nan Zhou Remotely operated vehicles (ROVs) are the most common underwater robots, excelling in complex environments. Hydrodynamic coefficients are crucial for predicting ROV performance. The motion model is established, simulated with specific methods, and results showed significant pressure resistance differences based on movement direction and speed, confirming simulation accuracy for design guidance.
17 - Multi-AUV Based Underwater Target Tracking Method via Reinforcement Learning in Dynamic Ocean Environment.
Authors: Tianxiang Xing, Jingzehua Xu, Jun Du, Xiangwang Hou, Tianyu Xing, Yong Ren Autonomous underwater vehicles (AUVs) are valuable for underwater tasks, but a single AUV has limited abilities. A multi-AUV system is used for complex tasks like target tracking, but existing studies don't reflect real ocean conditions. This paper presents a multi-AUV tracking method using reinforcement learning in dynamic environments. It involves modeling ocean currents and applying a Markov decision process for navigation. Simulation results show an 83% success rate, indicating improved tracking accuracy and robustness.
18 - Embracing the future: the rise of humanoid robots and embodied AI
Authors: Jiankun Wang, Chaoqun Wang, Weinan Chen, Qi Dou, Wenzheng Chi In an era of fast technological progress, humanoid robots and embodied artificial intelligence (AI) are advancing machine capabilities. These robots, designed to look and behave like humans, can transform sectors like healthcare and industry. They perform tasks efficiently, assist individuals with disabilities, offer companionship, and aid in therapy. These technologies combine AI with physical forms and address significant societal challenges.
21 - Ego-to-Exo: Interfacing Third Person Visuals from Egocentric Views in Real-time for Improved ROV Teleoperation.
Authors: Adnan Abdullah, Ruo Chen, Ioannis Rekleitis, and Md Jahidul Islam IUnderwater ROVs are unmanned vehicles for ocean exploration. This paper introduces a new teleoperation interface that provides third-person visuals and real-time ROV position information. It uses a 3D geometry algorithm with monocular Simultaneous Localization and Mapping (SLAM) for accurate navigation. The method has been validated through experiments in low-light conditions, showing benefits for remote operation. This interactive approach offers new research opportunities in underwater telerobotics.
22 - Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization.
Authors: Mingzhi Chen, Yuan Liu, Daqi Zhu, Anfeng Shen, Chao Wang, Kaimin Accurate parameter identification of underwater vehicles is important for controller design and fault diagnosis. Some research uses numerical simulations to find model parameters but often only perform single-degree-of- freedom steady-state simulations. This paper solves the velocity response of underwater vehicles using the overset grid and Dynamic Fluid-Body Interaction model. It introduces a quantum particle swarm optimization method to identify inertia and resistance parameters. The identified model's mean square error of forward velocity is less than 0. 20%, outperforming other methods.
35 - Solar-Powered ROV: Advancing Underwater Exploration with Renewable Energy
Authors: Sani Mohammed Lawal, Shima Valizadeh, Nazila Fough, Somasundar Kannan This paper presents a solar-powered solution for remotely operated vehicles (ROVs) used in underwater inspections and exploration. The Blue ROV can operate for 5 hours with solar power, supported by a miniaturized 10Hp/12kg design with an energy demand of 943.68W. Solar panels on floating platforms provide the necessary power, with detailed calculations for optimal panel size and number. The system includes inverters, charge controllers, and battery banks for continuous operation, promoting sustainable maritime activities.
36 - A Digital Twin Infrastructure for NGC of ROV during Inspection
Authors: David Scaradozzi, Flavia Gioiello, Nicolò Ciuccoli, and Pierre Drap Remotely operated vehicles (ROVs) can perform many tasks, though they need support ships and operators. Recent tech advancements aim to make ROVs semi- autonomous. This study presents a navigation, guidance, and control (NGC) system for the BlueROV2, solving challenges in power, communication, and stability. It uses a sensor system for localization, tethered communication for power, and a heading control strategy. A digital twin has been created for simulation and analysis, validated at ports in Marseille and Ancona. The NGC system showed improved stability and trajectory tracking, supporting future communication systems.
37 - Artificial Neural Network for Glider Detection in a Marine Environment by Improving a CNN Vision Encoder
Authors: Jungwoo Lee, Ji-Hyun Park, Jeong-Hwan Hwang, Kyoungseok Noh, , Youngho Choi, and Jinho Suh Much of the ocean is still unexplored, leading to the use of remotely operated vehicles (ROVs) and gliders for data collection. A safer and more efficient system for recovering gliders is needed. This study proposes a lightweight artificial neural network for detecting underwater gliders. The model achieved an average precision (AP) of 99.7%, outperforming the existing EfficientDet-D2 model, which had an AP of 69.2%. The results indicate high accuracy in glider detection.
38 - A Digital Twin Infrastructure for NGC of ROV during Inspection
Authors: David Scaradozzi, Flavia Gioiello, Nicolo Ciuccoli, and Pierre Drap This study presents a navigation, guidance, and control (NGC) system for a six DoF BlueROV2, aiming to make ROVs semi-autonomous and remotely test their dynamics. The system uses a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight guidance system. The control system uses PID controllers and a digital twin to visualize the ROV's movements and hydrodynamic behaviors. The system has proven positive vehicle stability and trajectory tracking, and has the potential for a future bidirectional communication system.
05 - Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
Authors: Jinxiong Gao, Xu Geng, Yonghui Zhang, and Jingbo Wang This document focuses on the importance of autonomous path planning for underwater vehicles, highlighting its significance in maritime conservation and monitoring. It discusses the challenges traditional algorithms face in marine environments and introduces a new method called bidirectional cached rapidly exploring random tree star (BCRRT*), which enhances the efficiency and adaptability of underwater navigation. The proposed BCRRT* method leverages bidirectional path planning and caching to optimize search processes and improve performance over existing algorithms, backed by simulation results that demonstrate its effectiveness.
06 - Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution
Authors: Malhar Khan, Muhammad Amir Raza, Ghulam Abbas, Salwa Othmen, Amr Yousef, and Touqeer Ahmed Jumani This article addresses the crucial role of pothole detection in ensuring the safety and efficiency of autonomous vehicles. It proposes a novel solution that utilizes the YOLO V8 deep learning algorithm for real-time identification of potholes, thereby minimizing potential hazards for vehicles and passengers. The study evaluates the performance of this system against existing methods, revealing its superior accuracy and efficiency. Furthermore, the authors explore various data augmentation techniques to enhance detection capability, concluding that YOLO V8 represents a significant advancement in pothole detection technologies for autonomous driving.