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Studies of ROV concepts 2025
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2 - Dynamic Target Hunting Under Autonomous Underwater Vehicle (AUV) Motion Planning Based on Improved Dynamic Window Approach (DWA).
Authors: Juan Li, Houtong Lu, Honghan Zhang, and Zihao Zhang A new method for hunting moving targets using multiple Autonomous Underwater Vehicles (AUVs) is proposed. It combines an improved Dynamic Window Approach (DWA) with Rapidly-exploring Random Tree (RRT) and includes collision avoidance rules. These rules help select safe paths, enhancing obstacle avoidance and reliability. A consistency algorithm ensures the task adapts if an AUV fails. The method effectively predicts target movements and allocates hunting points, resulting in efficient multi- AUV hunting strategies.
7 - Hydrodynamics Model Identification and Model-Based Control Application of a New Type of AUV
Authors: Lunyang Lin, Yuxiang Chen, Hong Xiong, Chunliang Yu, Hong Zhu, Yiyang Xing, and Guodong Zheng The hydrodynamic coefficients of underwater robots help assess their maneuverability and stability, aiding in controller design. This paper introduces an Autonomous Underwater Vehicle (AUV) with a negative-lift profile, using a method that integrates neural networks to identify hydrodynamic coefficients. A dynamic model is built for the AUV, and an MPC controller is designed. Simulations and pool tests show that despite errors in calculating hydrodynamic coefficients, the controller still effectively manages the AUV, proving the practicality of using CFD methods for these calculations in real applications.
1 - Coverage path planning for multi-AUV considering ocean currents and sonar performance.
Authors: Xukai Mu and Wei Gao Coverage path planning (CPP) for autonomous unmanned vehicles (AUV) takes into account sonar performance and ocean currents. This study suggests an integrated algorithm that combines the Dijkstra algorithm for the shortest path, Particle Swarm Optimization (PSO), and the ELKAI library to manage multi-AUV task allocation and path optimization, demonstrating effective results in simulations.
11 - Cooperative control for a ROV-based deep-sea mining vehicle with learned uncertain nonlinear dynamics.
Authors: Yuheng Chen, Haicheng Zhang, Weisheng Zou, Haihua Zhang, Daolin Xu This paper proposes an improved deep-sea mining system using a remotely-operated vehicle (ROV) to address track slippage issues in traditional systems. It presents a dynamic model for the ROV-based Deep-sea Mining Vehicle (RDMV) and introduces a cooperative control strategy. A distributed model predictive control (DMPC) controller and a learning-based model predictive control (LMPC) controller are developed, with a prediction function for external disturbances. The effectiveness of the LMPC controller is tested, while the cooperative strategy is validated through simulations.
5 - 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: Sae-Yong Park, Tae-Geon Chung, and Tae-Ho Im This study discusses improvements in Autonomous Underwater Vehicles (AUVs) and their communication capabilities. It introduces a method using Superimposed Adjusted-HFM signals for reliable underwater communication, particularly under Doppler conditions.
6 - ROV Teleoperation in the Presence of Cross-Currents Using Soft Haptics.
Authors: Joshua Brown, Ildar Farkhatdinov, Michael Jenkin The remote operation of underwater vehicles is challenging due to unseen environmental disturbances like cross-currents. This study investigates a new haptic touchpad that uses vibrations to inform operators about cross-currents. An experiment showed that haptic feedback significantly reduced cognitive load and improved positioning accuracy for operators. Although conducted under controlled conditions, this method can be applied in real-world situations to share live environmental data.
3 - A novel real-time intelligent detector for monitoring UAVs in live- line operation on 10 kV distribution networks.
Authors: Haibo Duan, Fanrong Shi, Bo Gao, Yingyue Zhou, Qiushi Cui The live-line operation of 10 kV distribution networks is crucial for steady power supply but faces challenges like poor monitoring and performance. This study introduces the FEM-YOLOv8 algorithm for detecting protective equipment in such operations. Deployed on UAV- compatible edge devices, it enhances monitoring. Key improvements include an upgraded FAST-C2f module for better feature extraction, an efficient channel attention mechanism for detection, and a new loss function for accuracy. The model shows improved precision, recall, and speed.
12 - Model-Based Offline Reinforcement Learning for AUV Path- Following Under Unknown Ocean Currents with Limited Data
Authors: Xinmao Li, Lingbo Geng, Kaizhou Liu, and Yifeng Zhao Minimizing experimental data while ensuring AUV safety in complex underwater environments is crucial. This study proposes a conservative offline model-based Q-learning (CMQL) algorithm for this purpose. CMQL is robust, utilizes data efficiently, and needs no fine-tuning. It uses limited AUV motion data for training, allowing accurate long-term predictions. The system's design ensures strong generalization and disturbance rejection. Simulation results show CMQL effectively follows paths under unknown currents with just 1000 data points, achieving zero-shot transfer for real-world applications.
13 - Target pursuit for multi-AUV system: zero-sum stochastic game with WoLF-PHC assisted.
Authors: Le Hong, Weicheng Cui. Utilizing multiple autonomous underwater vehicles (AUVs) to chase an invading vehicle is complex. This paper presents a system model using a zero-sum stochastic game framework to develop energy-efficient pursuit strategies. A relay-pursuit mechanism and reward function are designed to capture the invading vehicle quickly. The WoLF-PHC algorithm helps AUVs navigate unknown environments, and simulations show the approach's effectiveness and robustness.
8 - Implementation of ROV control from onshore bases: an innovative approach to subsea inspections and interventions.
Authors: Lucas dos Anjos Cristiano Marino, Hernandes Coutinho Fagundes, Vinicius Guedes Gusmão The work proposal aimed to pilot ROV vehicles from onshore control centers to reduce risks and improve decision-making. The team worked with ROV service providers to adapt existing systems and use a private 4G network for control. Challenges arose in network configuration and simulating offshore operations. In early 2023, Fugro and Subsea7 completed several subsea pipeline inspections. The pilot will continue with Oceaneering and explore new transmission technology. The initial phases showed advantages of piloting ROVs from land.
10 - Security of AUV-carried OIRS-assisted quantum key distribution links in underwater channels subject to misalignment.
Authors: Weina Pang, Ping Wang, Bo Bai, Wenwen Chen, Shuang Li, and Kaile Wang To create a secure communication link in underwater networks, quantum key distribution (QKD) protocols are used with underwater wireless optical communication (UWOC) systems. The reliance on line-of-sight creates challenges for this system. This work introduces an optical intelligent reflecting surface (OIRS) on an autonomous underwater vehicle (AUV) to reduce LOS issues and improve reliability for both discrete and continuous- variable QKD.
14 - Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
Authors: Zheng Fang, Tianhao Chen, Tian Shen, Dong Jiang, Zheng Zhang, and Guangliang Li Multiple autonomous underwater vehicles (multi-AUVs) can work together to achieve tasks a single AUV cannot. The Multi-Agent Generative Adversarial Imitation Learning (MAGAIL) method has limitations in needing optimal demonstrations. This letter suggests Multi-Agent Generative Adversarial Interactive Self-Imitation Learning (MAGAISIL), which allows AUVs to learn better through self-generated trajectories.
Authors: Yongcheng Cui, Ying Zhang, Cui-Hua Zhang, Simon X. Yang This paper reviews recent research on task cognition and planning for domestic service robots, focusing on technologies like command text parsing, active task cognition, multimodal perception, and action sequence generation. It analyzes challenges in command parsing, explores deep learning enhancements, and discusses research trends and task planning approaches. Finally, it summarizes current challenges and future development directions.
9 - Task cognition and planning for service robots.
4 - Stackelberg game-based anti-disturbance control for unmanned surface vessels via integrative reinforcement learning.
Authors: Yizhen Meng, Chun Liu, Jing Zhao, Jing Huang, Guanbo Jing Unmanned surface vessels (USVs) often face challenges from ocean waves that disrupt their intended path. To address this, a new control strategy using Stackelberg game theory is proposed. It includes a velocity error dynamic system that allows USVs to optimize control and reject disturbances through a leader-follower model. The strategy's effectiveness is confirmed by analyzing Nash equilibrium, and reinforcement learning is used to find the optimal solution. Simulation results show improved tracking accuracy and reduced energy use.
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