studies-rov

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

4 - Wide Field-of-View Air-to-Water Rolling Shutter-Based Optical Camera Communication (OCC) Using CUDA Deep-Neural- Network Long-Short-Term-Memory (CuDNNLSTM)

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).

5 - 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.

6 - 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.

7 - 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.

8 - 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..

9 - 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.

10 - 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.

11 - 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.

12 - 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.

15 - 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.

16 - 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.

17 - 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.

18 - 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.

19 - 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.

20 - 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.

21 - 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.

22 - 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.

23 - 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.

24 - 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..

25 - 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.

26 - 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.

Click on the octopus to return to the top of the page

13 - An effective fault-tolerant control with slime mold algorithm for unmanned underwater vehicle

Authors: Tianhong Zeng, Daqi Zhu, Chunhua Gu, Simon X. Yang This article discusses a novel fault-tolerant control strategy known as the Slime Mold Algorithm (FTC-SMA) designed to improve the resilience of multi-thruster unmanned underwater vehicles during thrust loss events. This strategy facilitates rapid thrust redistribution to maintain torque and sailing direction, even under severe propulsion system failures. By adhering to physical thruster limits, FTC-SMA effectively addresses over-actuation problems. The article validates the strategy’s effectiveness, efficiency, and stability through various simulation experiments, showcasing its superiority compared to existing algorithms like particle swarm optimization and grasshopper optimization.

27 - A systematic review of haptic texture reproduction technology

Authors: Si Chen, Tonghe Yuan, Lujin Xu, Weimin Ru, Dongqing Wang This article highlights techniques such as vibration, ultrasound, and electrostatic systems, and investigates the role of artificial intelligence and deep learning in enhancing the personalization of tactile feedback. The significance of understanding tactile perception mechanisms, specifically the function of Piezo proteins and the interaction between receptors and their environments, is emphasized for improving the accuracy of feedback systems. Moreover, challenges in accurately reproducing fine textures and high-frequency vibrations are addressed, pointing to the necessity of interdisciplinary research in fields like neuroscience, materials science, and AI for future progress in haptic technology.

28 - Environment sensing technology of underwater ROV based on artificial siding

Authors: Wenhui Wang, Fumeng Ye, & Rufei He This article discusses the limitations posed by high pressure and low temperature in underwater environments on sensor performance, leading to measurement inaccuracies. It introduces a novel underwater sensing technology for ROVs based on artificial side lines, detailing the terrain perception principles, parameter analysis, and the construction of an acoustic sensor network. This method significantly enhances environment perception accuracy, with measurement errors under 1% under various conditions. This technology shows promise for several applications, including underwater exploration and marine research, with suggestions for ongoing optimization and incorporation of additional environmental parameters

30 - The use of emerging autonomous technologies for ocean monitoring: insights and legal challenges

Authors: Aspasia Pastra, Tafsir Matin Johansson, Joana Soares, and Frank E. Muller-Karger This article explores the crucial function of Essential Ocean Variables (EOVs) in enhancing the understanding of marine ecosystems and emphasizes the importance of advanced observation technologies like Autonomous Underwater Vehicles (AUVs), Unmanned Aerial Vehicles (UAVs), and Maritime Autonomous Vehicles (MAVs). It highlights the significant legal and policy challenges associated with the integration of these technologies into Marine Scientific Research (MSR), identifying six key legal areas needing attention. To address these challenges effectively, the article advocates for a coordinated approach involving scientists, policymakers, and international organizations.

31 - Towards Autonomous Coordination of Two I-AUVs in Submarine Pipeline Assembly

Authors: Salvador López-Barajas, Alejandro Solis, Raúl Marín-Prades, Pedro J. Sanz The article explores the challenges in conducting inspection, maintenance, and repair (IMR) operations on underwater infrastructure, highlighting the limitations of remote-operated vehicles (ROVs) and professional divers. It introduces an innovative autonomous dual-I-AUV (Intervention-Autonomous Underwater Vehicle) system designed for the assembly of rigid pipeline segments in confined underwater environments. The system comprises two I-AUVs equipped with advanced manipulators that work in coordination to perform precise underwater tasks, demonstrating successful trials in a controlled water tank, which marks progress towards automated subsea construction.

33 - Translation-based multimodal learning: a survey

Authors: Zhengyi Lu, Yunhong Liao, & Jia Li The article provides a comprehensive survey of translation- based multimodal learning, focusing on two main paradigms: end-to-end translation and representation- level translation. End-to-end methods facilitate direct mappings between modalities using various advanced architectures, while representation-level methods align multimodal signals within a common embedding space, enhancing robustness against data noise. Highlighting insights from over forty benchmark studies, the article discusses two notable models—the xDSBMIT and TransTrans—demonstrating their effectiveness in multimodal translation tasks. Lastly, it identifies ongoing challenges and future directions for developing versatile and ethically sound multimodal systems.

34 - Powering Underwater Robotics Sensor Networks Through Ocean Energy Harvesting and Wireless Power Transfer Methods: Systematic Review

Authors: Sverrir Jan Nordfjord, Saemundur E. Thorsteinsson, and Kristinn Andersen The article discusses the rising global demand for innovative underwater applications, highlighting the energy challenges faced by underwater devices used in scientific research, commercial ventures, and defense operations. It focuses on two primary methods for powering these devices: energy harvesting from natural environmental sources and wireless power transfer (WPT). While energy harvesting is dependent on specific environmental conditions, WPT faces efficiency issues over distance. The paper concludes that further research is necessary to develop hybrid solutions that can effectively utilize multiple power sources.

35 - Underwater Acoustic Integrated Sensing and Communication: A Spatio-Temporal Freshness for Intelligent Resource Prioritization

Authors: Ananya Hazarika, & Mehdi Rahmati This article introduces an integrated sensing and communication (ISAC) framework specifically designed for underwater environments, which uses a new Spatio- Temporal Information-Theoretic Freshness metric. This approach shifts the focus from merely minimizing delays to maximizing the value of transmitted information. The framework includes intelligent strategies for power allocation, beamforming, and cooperative sensing, supported by multi-agent reinforcement learning for enhanced resource use. The proposed methods demonstrate significant performance improvements in energy efficiency and data relevance in realistic underwater scenarios.

36 - Influence of Marine Environmental Factors on Characteristics of Composite Magnetic Field of Underwater Vehicles

Authors: Honglei Wang, Xinyu Dong, and Yixin Yang This research study explores the spatiotemporal characteristics, attenuation laws, and influencing mechanisms of these fields. The study integrates three distinct types of magnetic fields: the magnetic anomaly field created by the ferromagnetic vehicle, the wake magnetic field from its motion, and the ocean wave magnetic field influenced by wind-driven waves. Findings reveal that vehicle motion parameters affect the magnetic anomaly and wake fields, while wind conditions significantly impact the ocean wave magnetic field. Overall, this work improves the understanding of how marine environmental factors and vehicle dynamics contribute to the magnetic fields during underwater navigation.

37 - Inductive Wireless Power Transfer for Autonomous Underwater Vehicles: A Comprehensive Review of Technological Advances and Challenges

Authors: Han Xu, Rong Zheng, & Bo Yang, and Wei Ning The article discusses the advancements in underwater inductive wireless power transfer (UIWPT) as a solution to enhance the endurance and efficiency of autonomous underwater vehicles (AUVs) by providing efficient energy replenishment. It systematically reviews the architecture of UIWPT systems, evaluates power loss mechanisms and optimization strategies, and summarizes recent research developments in relevant technologies. The discussion extends to environmental factors affecting UIWPT integration with AUV platforms and concludes with future trends and challenges in deploying reliable underwater power systems.

38 - Visual Signal Recognition with ResNet50V2 for Autonomous ROV Navigation in Underwater Environments

Authors: Cristian H. Sánchez-Saquín, Alejandro Gómez- Hernández, Tomás Salgado-Jiménez, Juan M. Barrera Fernández, Leonardo Barriga-Rodríguez, and Alfonso Gómez-Espinosa This article details the development and assessment of AquaSignalNet, a deep learning system designed for recognizing underwater visual commands, thus facilitating autonomous navigation for Remotely Operated Vehicles (ROVs). Utilizing a ResNet50 V2 architecture and a custom dataset, UVSRD, the system was trained on 33,800 labeled images representing various gesture classes. Deployed on a Raspberry Pi 4, AquaSignalNet demonstrated high success rates in navigating predefined paths, showing promise as a markerless solution for underwater tasks.

39 - An Underwater Salvage Robot for Retrieving Foreign Objects in Nuclear Reactor Pools

Authors: Ming Zhong, Zihan Gao, Zhengxiong Mao, Ruifei Lyu, and Yaxin Liu The article presents the development of an underwater salvage robot designed to locate and retrieve foreign objects in nuclear reactor pools. The robot features an ROV platform combined with a three-degree-of-freedom Delta robotic arm and utilizes advanced technologies for localization and object detection. Experimental results demonstrate the robot’s high effectiveness, achieving notable identification and retrieval rates of foreign objects.

40 - Deep learning methods for 3D tracking of fish in challenging underwater conditions for future perception in autonomous underwater vehicles

Authors: Martin Føre, Emilia May O’Brien, and Eleni Kelasidi Unmanned underwater vehicles (UUVs) are increasingly employed in fish farming to perform tasks unsuitable for human workers. However, there is concern that these vehicles could stress farmed fish by causing them to flee from intrusive objects. To address this issue, researchers have developed automated systems that can assess the distance between fish and the UUVs in real-time using modern camera technology and deep learning methods. This involves four innovative pipelines that facilitate the measurement of distances, which were validated against sonar data. The best-performing methods were integrated into the Robot Operating System (ROS2) to improve fish welfare and enhance control over UUVs.

41 - A Novel Cooperative Navigation Algorithm Based on Factor Graph and Lie Group for AUVs

Authors: Jiapeng Liu, Xiaodong Bu, & Chao Wu The article introduces a new cooperative navigation algorithm for multiple Autonomous Underwater Vehicles (AUVs) that is versatile and applicable to various configurations. Unlike traditional algorithms designed for specific forms (like parallel or leader-slave), this method utilizes factor graphs and Lie group theory to enhance AUV localization capabilities. By representing AUV motion states in the two-dimensional special Euclidean group and employing nonlinear optimization techniques, the new algorithm demonstrates substantial improvements in localization accuracy across different operational setups, significantly outperforming conventional Extended Kalman Filter (EKF) methods.

42 - A CFD-Based Surrogate for Pump–Jet AUV Maneuvering

Authors: Younhee Kwon, Dong-Hwan Kim, Jeonghwa Seo, and Hyun Chung This article presents a surrogate maneuvering model developed to predict the performance of autonomous underwater vehicles (AUVs) equipped with pump-jet propulsion. Traditional methods relying on high-fidelity computational fluid dynamics (CFD) are computationally expensive. The surrogate model provides comparable accuracy while significantly reducing costs, utilizing results from unsteady Reynolds-averaged Navier-Stokes equations and employing a Taylor-expansion-based framework. It improves the representation of propulsion and rudder forces and has been validated against high-fidelity simulations, demonstrating its ability to accurately forecast kinematic responses, forces, and moments.

43 - Deep Learning-Assisted ES-EKF for Surface AUV Navigation with SINS/GPS/DVL Integration

Authors: Yuanbo Yang, Bo Xu, Baodong Ye, & Feimo Li The article introduces a new cooperative navigation algorithm for multiple Autonomous Underwater Vehicles (AUVs) that is versatile and applicable to various configurations. Unlike traditional algorithms designed for specific forms (like parallel or leader-slave), this method utilizes factor graphs and Lie group theory to enhance AUV localization capabilities. By representing AUV motion states in the two-dimensional special Euclidean group and employing nonlinear optimization techniques, the new algorithm demonstrates substantial improvements in localization accuracy across different operational setups, significantly outperforming conventional Extended Kalman Filter (EKF) methods.

44 - Multi-AUV Cooperative Search for Moving Targets Based on Multi-Agent Reinforcement Learning

Authors: Le Li, Ruiqi An, Zhaozhi Guo, & Jian Gao The paper presents a novel convolutional multi-agent deep deterministic policy gradient method, termed PER- CMADDPG, designed for multi-autonomous underwater vehicle (AUV) cooperative search of moving targets. It establishes a mathematical model comprising environment, AUV, and information update systems. The PER-CMADDPG method innovates on the MADDPG framework by integrating convolutional neural networks (CNNs) for improved spatial awareness and employing a prioritized experience replay (PER) to enhance learning efficiency. Simulation results illustrate that this method accelerates convergence and achieves greater rewards, while also analyzing the effects of various operational parameters

45 - Robust Underwater Docking Visual Guidance and Positioning Method Based on a Cage-Type Dual-Layer Guiding Light Array

Authors: Ziyue Wang, Xingqun Zhou, Yi Yang, Zhiqiang Hu, Qingbo Wei, Chuanzhi Fan, Quan Zheng, Zhichao Wang, and Zhiyu Liao This paper introduces a cage-type dual-layer guiding light array that enhances visual localization as the Autonomous Underwater Vehicle (AUV) approaches the docking station. It outlines a dynamic localization algorithm designed to differentiate beacon appearance at various docking stages, applying advanced techniques like particle swarm optimization and a robust filtering strategy. The proposed method demonstrates high success rates in beacon matching, even under extreme conditions, facilitating reliable and continuous docking guidance for AUVs.

46 - Marine-Inspired Multimodal Sensor Fusion and Neuromorphic Processing for Autonomous Navigation in Unstructured Subaquatic Environments

Authors: Chandan Sheikder, Weimin Zhang, Xiaopeng Chen, Fangxing Li, Yichang Liu, Zhengqing Zuo, Xiaohai He, and Xinyan Tan This article proposes a novel neuromorphic framework inspired by marine species’ navigation strategies, emphasizing the co-design of advanced sensors and event-based neuromorphic processors. This framework aims to improve navigational robustness and energy efficiency by mimicking the multisensory fusion and processing capabilities found in marine life, suggesting significant advancements for applications like deep-sea exploration and underwater infrastructure inspection.

47 - Physics-Informed Dynamics Modeling: Accurate Long-Term Prediction of Underwater Vehicles with Hamiltonian Neural ODEs

Authors: Xiang Jin, Zeyu Lyu, Jiayi Liu, and Yu Lu This document introduces a physics-informed framework that models the dynamics of an Autonomous Underwater Vehicle (AUV) through the discovery of its energy landscape, integrating Port-Hamiltonian mechanics into a neural ordinary differential equation (NODE) structure. This approach allows for rigorous identification of the AUV’s Hamiltonian and associated physical matrices from observational data, achieving superior accuracy and consistency compared to traditional black-box deep learning models, especially during complex maneuvers.

48 - Distributed Adaptive Fault-Tolerant Formation Control for Heterogeneous USV-AUV Swarms Based on Dynamic Event Triggering

Authors: Haitao Wang, Hanyi Wang, & Xuan Guo This article discusses a novel approach to cooperative formation control for heterogeneous unmanned systems, specifically Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs), addressing challenges such as actuator faults, varying communication topologies, and communication delays. A unified dynamic model utilizing the Euler–Lagrange framework is developed, along with a Distributed Adaptive Fault- Tolerant Control (DAFTC) that incorporates a Dynamic Event-Triggered Mechanism (DETM) to optimize communication efficiency while maintaining robustness through online fault compensation.

2 - Smooth and efficient motion planning of large-scale and cooperative multi-arm tunnel drilling robot

Authors: Yuming Cui, Jiajun Pu, Ningning Hu, Menghao Cui To address the motion planning challenges in multi-arm cooperative operations of tunnel rock-drilling robots, the document’s authors established forward/inverse kinematics models for drilling arms using an enhanced Denavit-Hartenberg method combined with radial basis function neural networks. A joint-space collision avoidance strategy is proposed using an enhanced artificial potential field (IAPF) method that incorporates both attractive and repulsive potential fields.

3 - Joint optimization of AoI and energy for AUV-assisted data collection in underwater acoustic sensor networks

Authors: Weinan Cao, Keyu Chen, and En Cheng For the sake of jointly optimizing the age of information (AoI) and energy consumption, we propose a multi- autonomous underwater vehicle (AUV)-assisted underwater data collection scheme in this paper. Simulation results show that the proposed algorithm outperforms three benchmark algorithms in terms of average AoI and collection utility. Additionally, the energy consumption of sensor nodes is more balanced, and the network lifetime is extended.

14 - Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning

Authors: Pengyan Dong, Jiahong Liu, Hang Tao, Yang Zhao, Zhijie Feng, and Hanjiang Luo This paper proposes a long-endurance collaborative search and rescue (SAR) scheme for maritime unmanned systems, integrating UAVs and USVs to leverage their complementary vision-sensing capabilities—UAVs for wide-area aerial surveillance and USVs for precise surface- level operations. A multi-agent reinforcement learning (MARL)-based approach enables UAV swarms to conduct cooperative first-phase search using probabilistic maps, while USVs refine these maps for accurate second-phase localization. To overcome UAV energy limitations, a MARL-driven multi-USV charging scheduling method is introduced to extend UAV flight time.

29 - Hydrodynamic Numerical Analysis of AUV Underwater Docking with Conical Docking Bay

Authors: Yang Gao, Xiaohu Li, Jianwei Mei, Daohua Lu, and Yanbing Tang This paper analyzes how docking geometry parameters and ocean currents affect the hydrodynamics of an autonomous underwater vehicle (AUV) during underwater docking with a conical bay. Using a 3D mathematical model and CFD methods, the study examines impacts on docking resistance, lift, velocity, and pressure fields. Key findings include: a larger taper angle reduces drag, while a wider inlet diameter increases it; higher current speeds amplify drag variations, and greater direction angles create significant lift due to velocity differences between upper and lower flow fields. The results offer theoretical insights for optimizing AUV docking structures and control strategies.

32 - Deformable USV and Lightweight ROV Collaboration for Underwater Object Detection in Complex Harbor Environments: From Acoustic Survey to Optical Verification

Authors: Yonghang Li, Mingming Wen, Peng Wan, Zelin Mu, Dongqiang Wu, Jiale Chen, Haoyi Zhou, Shi Zhang, and Huiqiang Yao This paper presents a USV-ROV collaborative system for efficient underwater inspection and object disposal in complex harbor environments. The deformable USV, equipped with side-scan sonar (SSS) and multibeam echo sounder (MBES), enables rapid large-area surveying and high-resolution 3D seabed mapping; it accurately localized and preliminarily identified simulated objects with ≤2.2 m positioning deviation. The lightweight ROV, fitted with an optical camera, forward-looking sonar, and a manipulator, performed close-range verification and disposal, confirming object identity and location with ≤3.2 m deviation from SSS estimates.

49 - Research and Development of a Autonomous Underwater Vehicle

Authors: Mustafa Cenik, Mehmet Talha Yilmaz, Umut Can Senuzar, Ömer Eroglu, and Orçin Uygun This study outlines the design of a fully autonomous unmanned underwater vehicle (UUV) aimed at enhancing automation in aquaculture, aquatic scientific research, and national defense. The prototype uses a Raspberry Pi 5 for AI-driven image processing (via Raspberry Pi Camera V2) and real-time sensor data handling—including motor control, orientation, depth, and temperature. Autonomous navigation and decision- making are implemented through embedded Python and AI algorithms. Three-axis movement is enabled by a Pixhawk PX4 flight controller and six motors.

50 - Digital twin-driven swarm of autonomous underwater vehicles for marine exploration

Authors: Jing Yan, Tianyi Zhang, Xinping Guan, Xian Yang & Cailian Chen This article proposes a digital twin (DT)-driven swarm control framework for autonomous underwater vehicles (AUVs) to address two key challenges in marine exploration: high communication energy consumption and limited environmental perception. It constructs DT models for each AUV by fusing dynamics and environmental data; employs a parameter estimator to predict underwater flow fields; designs an energy-efficient swarm networking protocol; introduces an integral reinforcement learning (IRL)-based controller for coordinated virtual and physical AUV operation; and applies a virtual-real error optimization algorithm to enhance model fidelity.

51 - Disturbance-Rejection Control Strategies and Algorithms for Autonomous Underwater Vehicles and Unmanned Aerial Vehicles: A Cross-Domain Survey

Author: Yanzhou Wang This paper reviews disturbance-rejection control strategies for autonomous underwater vehicles (AUVs) and unmanned aerial vehicles (UAVs) operating in fluid- disturbed environments. It contrasts the two platforms' dynamic structures, medium properties, and disturbance types—highlighting differing requirements for low- frequency and high-frequency disturbances. The study examines key control methods, including sliding mode control (SMC), model predictive control (MPC), active disturbance rejection control (ADRC), and intelligent approaches like deep reinforcement learning and neural networks, assessing their robustness and applicability. Comparative analyses and guidelines are provided to aid practical control strategy design.

52 - The Provision of Physical Protection of information During the Transmission of Commands to a Group of UAVs Using Fiber Optic Communication Within the Group

Author: Dina Shaltykova, Aruzhan Kadyrzhan, Yelizaveta Vitulyova, and Ibragim Suleimenov This paper presents a novel method for the precise localization of remote radio-signal sources using a formation of unmanned aerial vehicles (UAVs). By studying the asymptotic intersections of these hyperbolas, the method ensures unique determination of the source position, even in the presence of multiple intersection points. Theoretical analysis confirms that the correct intersection point is located at a significantly larger distance from the UAV formation center compared to spurious intersections, providing a rigorous criterion for resolving localization ambiguity.