studies-rov-concepts

Studies of ROV concepts 2025 - Part B

1 - Control and Real-Time Monitoring of Autonomous Underwater Vehicle Through Underwater Wireless Optical Communication

Authors: Dongwook Jung, Rouchen Zhang, Hyunjoon Cho, Daehyeong Ji, Seunghyen Kim, and Hyeungsik Choi This paper introduces a new control structure using underwater wireless optical communication (UWOC) for autonomous underwater vehicle (AUV) motion control in underwater missions. The UWOC system transfers control inputs for the AUV's attitude from outside the water, while orientation data and visual images are sent to the control station outside the water. The system uses a return-to-zero modulation scheme for faster, longer- range data transfer. The hovering control performance of the AUV with the UWOC system was experimentally evaluated

2 - Design and Performance Verification of A-HFM Signals for Simultaneous Frame Detection, Cell ID Assignment, and Doppler Estimation in AUVs Using Multiple Surface Buoys.

Authors: Yung-Jie Chen, Yu-Han Lin, Guo-Liang Shih, Chi- Wai Chow, and Chien-Hung Yeh This work proposes a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long- Short-Term-Memory (CuDNNLSTM). The system can support a transmission rate of 7.2 kbit/s through a still water surface and 6.6 kbit/s through a wavy water surface, satisfying the hard-decision forward error correction (HD-FEC) bit-error-rate (BER).

3 - Model-Based AUV Path Planning Using Curriculum Learning and Deep Reinforcement Learning on a Simplified Electronic Navigation Chart

Authors: Lukasz Marchel, Rafał Kot, Piotr Szymak, and Paweł Piskur This paper evaluates reinforcement learning (RL)-based methods for autonomous underwater vehicles (AUVs) trajectory planning. It incorporates a full motion model and proposes a reward function to elicit desired behaviors. The models are trained on simplified ENC maps and compared against five classical methods. The proposed solution enables efficient, collision-free route planning.

4 - Enhanced Real-Time Simulation of ROV Attitude and Trajectory Under Ocean Current and Wake Disturbances

Authors: Yujing Zhao, Shipeng Xu, Xiaoben Zheng, Lisha Luo, Boyan Xu, and Chunru Xiong This study addresses issues in existing simulation systems for remotely operated underwater vehicles (ROVs) by developing a three-dimensional path simulation system for ocean inspection robots. The system optimizes the drag matrix by decomposing sea currents and simulating relative velocity in each direction. The system improves trajectory evolution stability and consistency, verifies control algorithm applicability, and provides reliable data support for ROV design and optimization. It also lays a solid foundation for developing intelligent underwater robots using IoT technology.

5 - Evaluation of the Nusantara 3 Remotely Operated Vehicle (N3- ROV): Special Report on Performance and Stability in Various Water Conditions

Authors: Xavercius Cezar Pratama, Indra Jaya, Muhammad Iqbal This study aimed to test the performance of the N3-ROV, a Remotely Operated Underwater Vehicle (ROV), in two different environments: a pool environment and a field environment. The N3-ROV had dimensions of 61x65x34 cm, 13 kg, and 128.4 kg.m/s² buoyancy. Performance tests showed that the N3-ROV had a straight movement in the pool environment with a 0° error, but had higher delay and error in the turning motion in the pool environment.

6 - A lightweight YOLO network using temporal features for high- resolution sonar segmentation

Authors: Sen Gao, Wei Guo, Gaofei Xu, Ben Liu, Yu Sun, and Bo Yuan A novel YOLO-based segmentation framework addresses the speed-accuracy trade-off in sonar image segmentation for autonomous underwater vehicles (AUVs). The framework uses a lightweight backbone network and bypass BiLSTM network for temporal feature learning, reducing processing latency and maintaining competitive segmentation accuracy..

7 - Three-Phase High Power Underwater Capacitive Wireless Power Transfer System for Autonomous Underwater Vehicles

Authors: Lei Yang, Liye Tian, Xinze Chen, Zhixue Bu, Dengrui Xing, Aimin Zhang, and Xiangqian Tong The paper presents a 1000 W high-frequency three-phase underwater capacitive wireless power transfer system for autonomous underwater vehicles (AUVs). The system's columnar structure and curved coupling coupler enhance mutual capacitance and power transfer capacity, reducing installation space and enhancing wireless power transfer capacity.

8 - Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles

Authors: Bo Wang, Junchao Peng, Jing Zhou, and Liming Zhao This paper explores the design and agile motion control of autonomous underwater vehicles (AUVs) in subsea environments. It uses Sequential Quadratic Programming (SQP) and Computational Fluid Dynamics (CFD) software to solve propeller arrangement optimization. The AET- NMPC algorithm significantly enhances response capability and control accuracy, reducing trajectory tracking error to 4.89%. This research provides insights for sophisticated underwater operations in challenging environments.

9 - Energy-Optimized Path Planning and Tracking Control Method for AUV Based on SOC State Estimation

Authors: Guangyi Yang, Zhenning Xu, Feng Wang, and Xiaoyu Zhang This paper proposes a path planning and trajectory tracking control framework for autonomous underwater vehicles (AUVs) combined with battery state of charge optimization. The framework uses the Grasshopper Optimization Algorithm (GOA) and the Artificial Potential Field Algorithm (APF) to achieve global path planning and local path optimization while minimizing energy consumption. The model predictive control (MPC) method ensures accurate tracking and dynamic management of SOC states. Simulation results show the framework outperforms traditional methods.

10 - Underwater SLAM Meets Deep Learning: Challenges, Multi- Sensor Integration, and Future Directions

Authors: Mohamed Heshmat, Lyes Saad Saoud, Muayad Abujabal, Atif Sultan, Mahmoud Elmezain, Lakmal Seneviratne, and Irfan Hussain This survey analyzes the latest developments in deep learning-enhanced SLAM for underwater applications, categorizing approaches based on methodologies, sensor dependencies, and integration with deep learning models. It highlights the benefits and limitations of existing techniques, highlights key innovations, and unresolved challenges. The survey introduces a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs), enhancing localization, mapping, and real-time data sharing among AUVs. The survey identifies gaps in current research and suggests future directions for developing robust underwater SLAM solutions.

11 - Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms

Authors: Félix Orjales, Julián Rodríguez-Cortegoso, Enrique Fernández-Pérez, Alejandro Romero, and Vicente Diaz-Casas This paper proposes a comprehensive methodology for obtaining hydrodynamic coefficients of underwater vehicles, combining empirical measurements and evolutionary algorithms. The methodology is applied to a commercially available remotely operated vehicle, BlueROV2, and analyzed for its adequacy, including comparisons to the real vehicle.

12 - Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems

Authors: Haoqian Huang, Bing Wang, and Yuan Yang This paper proposes a novel approach for underactuated autonomous underwater vehicles (AUVs) to improve formation control. By integrating Decision-Making (DO) with the formation controller, the system can handle nonlinear dynamics and ensure system stability. The approach addresses challenges such as assuming diagonal mass and damping matrices, relying on position measurements, and converge speed.

13 - Task Allocation and Path Planning Method for Unmanned Underwater Vehicles

Authors: Feng Liu, Wei Xu, Zhiwen Feng, Changdong Yu, Xiao Liang, Qun Su, and Jian Gao This study proposes a task allocation and path planning method for Unmanned Underwater Vehicles (UUVs) to improve their autonomous navigation and adaptability in complex marine environments. The method uses an Improved Grey Wolf Algorithm (IGWA) and a Circle chaotic mapping strategy to optimize task allocation and search efficiency. The GR-RRT* algorithm is employed for path planning, ensuring obstacle avoidance and reducing redundant sampling. Experimental results show significant advantages in task completion rate and path optimization efficiency.

14 - AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things

Authors: Talal S. Almuzaini, and Andrey V. Savkin This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) and an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads in an uneven seafloor. The approach uses a forward Dynamic Programming algorithm to optimize the AUV's trajectory and USV's start and end positions, maximizing residual VoI. Simulation results show the strategy outperforms conventional baselines for effective data collection in challenging underwater environments.

15 - Hydrodynamic Calculation and Analysis of a Complex-Shaped ROV Moving near the Wall Based on CFDs

Authors: Mengjie Jiang, Chaohe Chen, Zhijia Suo and Yingkai Dong A study using computational fluid dynamics (CFDs) analyzes the hydrodynamic characteristics of remotely operated vehicles (ROVs) near walls, considering factors like structural asymmetry, speed, and distance. Results show that wall influence on hydrodynamic forces is significant, with pressure differential resistance being the dominant factor affecting performance. This provides insights into calculating hydrodynamic coefficients and modeling complex shapes operating near walls.

16 - Better interaction experience: human-machine interface for soft robotic systems

Authors: Zeyu Gao, Zhiyuan Liao, Chunquan Li Soft robots are emerging as a key robotics technology due to their soft materials and unique drive methods. They adapt to unstructured environments and interact with humans safely, opening new directions in robotics development. Sensor performance plays a crucial role in human-machine interface (HMI), and new materials and technologies refine HMI, enhancing soft robot systems' possibilities.

17 - Hierarchical Adaptive Fixed-Time Formation Control for Multiple Underactuated Autonomous Underwater Vehicles Under Uncertain Disturbances and Input Saturation

Authors: Jiacheng Chang, Lanyong Zhang, Yifan Tan, Xue Fu, and Hongjun Yu This paper proposes a novel hierarchical framework for adaptive fixed-time formation control for multiple autonomous underwater vehicles (AUVs). The framework divides formation requirements and individual control challenges into two layers: Collision-free Formation Trajectories Generation (CFTG) and Adaptive Trajec-tories Tracking (ATT). The CFTG layer uses a consensus-based controller, an improved artificial potential field method, and an auxiliary compensation system to meet complex formation tasks. Simulations demonstrate the effectiveness and performance of the proposed method.

18 - ROVs Utilized in Communication and Remote Control Integration Technologies for Smart Ocean Aquaculture Monitoring Systems

Authors: Yen-Hsiang Liao, Chao-Feng Shih, Jia-Jhen Wu, Yu-Xiang Wu, Chun-Hsiang Yang, and Chung- Cheng Chang This study presents an intelligent aquatic farming surveillance system that integrates remotely operated vehicles, sensors, and real-time data transmission. It uses a mobile communication architecture with buoy relay stations for distributed edge computing, supports future upgrades to Beyond 5G and satellite networks, features a multi-terminal control system for monitoring anytime, anywhere, and is cost-effective. The system has successfully processed 324,800 data transmission events, demonstrating excellent performance in real-world production environments. It reduces operating costs and improves aquaculture efficiency.

19 - Energy-Optimized Path Planning for Fully Actuated AUVs in Complex 3D Environments

Authors: Shuo Liu, Zhengfei Wang, Tao Wang, Shanmin Zhou, Yu Zhang, Pengji Jin, and Guanjun Yang This paper presents an energy-optimized path planning approach for fully actuated autonomous underwater vehicles (AUVs) in three-dimensional ocean environments. It uses real terrain data and environmental models to develop a mathematical model of the AUV's total energy consumption. The proposed algorithm combines EOPSO and QP to identify the optimal path and surge speed, demonstrating superior robustness and optimized energy consumption.

20 - Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection

Authors: Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li, and Kangshun This paper introduces a hybrid framework that combines a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to improve autonomous underwater vehicles (AUVs) in dynamic environments. The MEO-BIT* component provides efficient global path planning, while the DQN component facilitates dynamic obstacle detection and adaptive local navigation. Experimental validation in asimulated Achao waterway (Chile) demonstrated the system's superiority, achieving a 46% reduction in collision rates, 15.7% improvement in path smoothness, and 78.9% faster execution time compared to conventional methods..

21 - Pectoral Fin-Assisted Braking and Agile Turning: A Biomimetic Approach to Improve Underwater Robot Maneuverability

Authors: Qu He, Yunpeng Zhu, Weikun Li, Weicheng Cui, and Dixia Fan The study investigates the integration of biomimetic pectoral fins into SpineWave robotic fins, enhancing maneuverability, stability, and braking efficiency in underwater robotics. The 1-DOF pectoral fin mechanism reduces stopping distance by 50%, enhancing agility and control, and laying the groundwork for adaptive fin designs and real-time control strategies.

22 - Multi-Information-Assisted Bistatic Active Sonar Target Tracking for Autonomous Underwater Vehicles in Shallow Water

Authors: Zhanpeng Bao, Yonglin Zhang, Yupeng Tai, Jun Wang, Haibin Wang, Chao Li, Chenghao Hu, and Peng Zhang This paper proposes a multi-information-assisted target tracking algorithm for bistatic active sonar, leveraging spatial and temporal echo signal structures to improve underwater surveillance for autonomous underwater vehicles (AUVs). The algorithm integrates target position, echo amplitude, and Doppler information during AUV movement, improving association probability computation efficiency. The algorithm shows a 23.95% performance improvement over traditional probabilistic data association, enhancing tracking autonomy and advancing AUVs' capability in ocean engineering applications.

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