Research Status and Development Trends of Deep-sea Unmanned Equipment Control System

WANG Biao; LUO Ruilong; WANG Fang; CUI Weicheng

doi:10.11993/j.issn.2096-3920.2025-0074

Volume 33 Issue 3

Jun 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 390-399

WANG Biao, LUO Ruilong, WANG Fang, CUI Weicheng. Research Status and Development Trends of Deep-sea Unmanned Equipment Control System[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 390-399. doi: 10.11993/j.issn.2096-3920.2025-0074

Citation:

WANG Biao, LUO Ruilong, WANG Fang, CUI Weicheng. Research Status and Development Trends of Deep-sea Unmanned Equipment Control System[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 390-399. doi: 10.11993/j.issn.2096-3920.2025-0074

Citation:

PDF( 854 KB)

Research Status and Development Trends of Deep-sea Unmanned Equipment Control System

doi: 10.11993/j.issn.2096-3920.2025-0074

1.
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2.
School of Engineering, Westlake University, Hangzhou 310030, China

Received Date: 2025-05-29
Accepted Date: 2025-06-09
Rev Recd Date: 2025-06-07

Available Online: 2025-06-11

Abstract

Abstract

Deep-sea unmanned equipment, as a strategic reflection of a nation’s marine scientific and technological strength, has been widely integrated into core fields such as resource exploration, marine scientific research, military security, and economic development. The control system, serving as the neural center for complex underwater operations, directly determines the mission execution efficiency of the equipment. This paper systematically combed the control theory system of deep-sea unmanned equipment, including technical paths such as traditional proportional-integral-derivative (PID) control, model-based control, data-driven intelligent control, and multi-agent control. It deeply analyzed the technical characteristics and engineering applicability of centralized, hierarchical, distributed, and hybrid control architectures. By comparing and analyzing the research status of key technologies such as navigation and positioning, communication transmission, and energy supply, the paper revealed common challenges in the industry, including model uncertainty, robust control performance, and multi-equipment collaboration mechanisms. The study shows that future control systems will develop towards deep empowerment of artificial intelligence, clustered collaborative operations, integration of new communication and energy technologies, and interdisciplinary innovation, providing theoretical and technical support for the intelligent transformation of deep-sea equipment.
- deep-sea unmanned equipment,
- control theory,
- control architecture,
- key technology

FullText(HTML)

References(64)

References

[1]	梁波, 赵宏宇, 王楠. 水下机器人在中国的早期发展[J]. 科学, 2022(3): 53-56.
[2]	NAKAMURA M, KOTERAYAMA W, YAMAMOTO I, et al. Control of heading angle of launcher of deep sea exploration unmanned underwater vehicle “KAIKO”[C]//Proceedings of The Sixteenth 2006 International Offshore and Polar Engineering Conference. San Francisco, CA, USA: ISOPE, 2006: 213-220.
[3]	FENUCCI D, FANELLI F, CONSENSI A, et al. A multi-platform guidance, navigation and control system for the autosub family of autonomous underwater vehicles[J]. Control Engineering Practice, 2024, 146: 105902. doi: 10.1016/j.conengprac.2024.105902
[4]	ALLEN B, STOKEY R, AUSTIN T, et al. REMUS: A small, low cost AUV; system description, field trials and performance results[C]//Oceans' 97. MTS/IEEE Conference Proceedings. Halifax, NS, Canada: IEEE, 1997: 994-1000.
[5]	WHITCOMB L L, JAKUBA M V, KINSEY J C, et al. Navigation and control of the Nereus hybrid underwater vehicle for global ocean science to 10903 m depth: Preliminary results[C]//2010 IEEE International Conference on Robotics and Automation. Anchorage, Alaska, USA: IEEE, 2010: 594-600.
[6]	任峰, 张莹, 张丽婷, 等. “海龙Ⅲ”号ROV系统深海试验与应用研究[J]. 海洋技术学报, 2019, 38(2): 30-35. REN F, ZHANG Y, ZHANG L T, et al. Research on the deep-sea test and application of the "Hailong Ⅲ" ROV system[J]. Journal of Ocean Technology, 2019, 38(2): 30-35.
[7]	梁一飞, 李永龙, 王皓冉, 等. 基于降阶扩张状态观测器的水下机器人自抗扰运动控制[J]. 传感器与微系统, 2024, 43(8): 141-145. LIANG Y F, LI Y L, WANG H R, et al. Active disturbance rejection motion control of ROV based on reduced order extended state observer[J]. Transducer and Microsystem Technologies, 2024, 43(8): 141-145.
[8]	黄陶俊, 石凯, 乌云嘎, 等. 基于自抗扰控制的AUV抗洋流对接研究[J]. 舰船科学技术, 2024, 46(14): 89-96. HUANG T J, SHI K, WU Y G, et al. Research on AUV docking of anti ocean current based on active disturbance rejection control[J]. Ship Science and Technology, 2024, 46(14): 89-96.
[9]	JING A, WANG J, GAO J, et al. Self-tuning adaptive active disturbance rejection pitch control of a manta-ray-like underwater glider[J]. Ocean Engineering, 2022, 254: 111364. doi: 10.1016/j.oceaneng.2022.111364
[10]	王翻, 武建国, 王晓鸣, 等. 改进型自抗扰在ROV位姿控制中的应用[J]. 舰船科学技术, 2024, 46(14): 91-98. WANG F, WU J G, WANG X M, et al. Application of improved active disturbance rejection in ROV pose control[J]. Ship Science and Technology, 2024, 46(14): 91-98.
[11]	褚悦, 石泽林, 王孟军, 等. 水下航行器有限时间滑模控制[J]. 水下无人系统学报, 2023, 31(6): 878-884. doi: 10.11993/j.issn.2096-3920.2022-0060 CHU Y, SHI Z L, WANG M J, et al. Finite-time sliding mode control for undersea vehicles[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 878-884. doi: 10.11993/j.issn.2096-3920.2022-0060
[12]	JOE H, KIM M, YU S C. Second-order sliding-mode controller for autonomous underwater vehicle in the presence of unknown disturbances[J]. Nonlinear Dynamics, 2014, 78(1): 183-196. doi: 10.1007/s11071-014-1431-0
[13]	TAHERI E, FERDOWSI M H, DANESH M. Design boundary layer thickness and switching gain in SMC algorithm for AUV motion control[J]. Robotica, 2019(10): 1-19.
[14]	孙旭瑶. 基于高阶滑模控制的水下机器人轨迹跟踪算法研究[D]. 秦皇岛: 燕山大学, 2023.
[15]	DENG S Y, HAO L Y, SHEN C. Autonomous underwater vehicle(AUV) motion design: Integrated path planning and trajectory tracking based on model predictive control(MPC)[J]. Journal of Marine Science & Engineering, 2024, 12(9): 1655.
[16]	XIN G, ZHOU M, YANG B, et al. Energy optimization control algorithm of underwater vehicle based on model predictive control[J]. Journal of Coastal Research, 2020, 103(SI): 830-834.
[17]	LIU Z, ZHU D, LIU C, et al. A novel path planning algorithm of AUV with model predictive control[J]. International Journal of Robotics and Automation, 2022, 37(6): 460-467.
[18]	ZHANG W, WANG Q, WU W, et al. Event-trigger NMPC for 3-D trajectory tracking of UUV with external disturbances[J]. Ocean Engineering, 2023, 283: 115050. doi: 10.1016/j.oceaneng.2023.115050
[19]	BIAN Y, ZHANG J, HU M, et al. Self-triggered distributed model predictive control for cooperative diving of multi-AUV system[J]. Ocean Engineering, 2023, 267: 113262. doi: 10.1016/j.oceaneng.2022.113262
[20]	TABATABA’I-NASAB F S, KEYMASI KHALAJI A, MOOSAVIAN S A A. Adaptive nonlinear control of an autonomous underwater vehicle[J]. Transactions of the Institute of Measurement and Control, 2019, 41(11): 3121-3131. doi: 10.1177/0142331218823869
[21]	ZHENG X, TIAN Q, ZHANG Q. Development and control of an innovative underwater vehicle manipulator system[J]. Journal of Marine Science and Engineering, 2023, 11(3): 548. doi: 10.3390/jmse11030548
[22]	LI J, WANG Y, LI H, et al. Sliding mode control with adaptive-reaching-law-based fault-tolerant control for AUV sensors and thrusters[J]. Journal of Marine Science and Engineering, 2023, 11(11): 2170. doi: 10.3390/jmse11112170
[23]	LU D, XIONG C, ZENG Z, et al. Adaptive dynamic surface control for a hybrid aerial underwater vehicle with parametric dynamics and uncertainties[J]. IEEE Journal of Oceanic Engineering, 2019, 45(3): 740-758.
[24]	熊保星, 甘文洋, 陈铭治, 等. 基于模糊线性自抗扰的水下机器人定深控制[J]. 控制工程, 2024: 1-9. XIONG B X, GAN W Y, CHEN M Z, et al. Depth control of underwater vehicle based on fuzzy linear active disturbance rejection[J]. Control Engineering, 2024: 1-9.
[25]	JI D, ZHOU S, REN J, et al. A prototype of newly dynamic underwater vehicle using fuzzy PID control[C]//2019 IEEE 28th International Symposium on Industrial Electronics(ISIE). Vancouver, BC, Canada: IEEE, 2019: 1121-1126.
[26]	CHENG C, SHA Q, HE B, et al. Path planning and obstacle avoidance for AUV: A review[J]. Ocean Engineering, 2021, 235: 109355. doi: 10.1016/j.oceaneng.2021.109355
[27]	LONDHE P S, SANTHAKUMAR M, PATRE B M, et al. Task space control of an autonomous underwater vehicle manipulator system by robust single-input fuzzy logic control scheme[J]. IEEE Journal of oceanic engineering, 2016, 42(1): 13-28.
[28]	HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647
[29]	郑雨帆, 王银涛, 孙琦. 基于轻量化深度网络的水下声呐目标识别方法[J]. 指挥控制与仿真, 2025: 1-10. ZHENG Y F, WANG Y T, SUN Q. Underwater sonar target recognition method based on lightweight deep network [J]. Command Control & Simulation, 2025: 1-10.
[30]	李培坤, 李锋, 葛忠显, 等. 基于改进YOLOv8n的水下目标检测算法[J]. 电子测量技术, 2025, 48(3): 172-179. LI P K, LI F, GE Z X, et al. Underwater target detection algorithm based on improved YOLOv8n[J]. Electronic Measurement Technology, 2025, 48(3): 172-179.
[31]	WANG N, CHEN T, LIU S, et al. Deep learning-based visual detection of marine organisms: A survey[J]. Neurocomputing, 2023, 532: 1-32. doi: 10.1016/j.neucom.2023.02.018
[32]	张天驰, 刘宇轩. 深度学习驱动的水下图像处理研究进展[J]. 计算机科学, 2024, 51(z1): 271-282. doi: 10.11896/jsjkx.230400107 ZHANG T C, LIU Y X. Research progress of underwater image processing based on deep learning[J]. Computer Science, 2024, 51(z1): 271-282. doi: 10.11896/jsjkx.230400107
[33]	ANWAR S, LI C. Diving deeper into underwater image enhancement: A survey[J]. Signal Processing: Image Communication, 2020, 89: 115978. doi: 10.1016/j.image.2020.115978
[34]	XIA S, ZHOU X, SHI H, et al. A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle[J]. Ocean Engineering, 2021, 233: 109049. doi: 10.1016/j.oceaneng.2021.109049
[35]	潘云伟, 李敏, 曾祥光, 等. 基于人工势场和改进强化学习的自主式水下潜航器避障和航迹规划[J]. 兵工学报, 2025, 46(4): 72-83. PAN Y W, LI M, ZENG X G, et al. AUV obstacle avoidance and path planning based on artificial potential field and improved reinforcement learning[J]. Acta Armamentarii, 2025, 46(4): 72-83.
[36]	孙玉山, 冉祥瑞, 张国成, 等. 智能水下机器人路径规划研究现状与展望[J]. 哈尔滨工程大学学报, 2020, 41(8): 1111-1116. doi: 10.11990/jheu.201906048 SUN Y S, RAN X R, ZHANG G C, et al. Research status and prospect of path planning for autonomous underwater vehicles[J]. Journal of Harbin Engineering University, 2020, 41(8): 1111-1116. doi: 10.11990/jheu.201906048
[37]	WANG Y, GAO J. Reinforcement-learning-based visual servoing of underwater vehicle dual-manipulator system[J]. Journal of Marine Science and Engineering, 2024, 12(6): 940. doi: 10.3390/jmse12060940
[38]	闫敬, 徐龙, 曹文强, 等. 基于深度强化学习的多潜器编队控制算法设计[J]. 控制与决策, 2023, 38(5): 1457-1463.
[39]	SONG D, GAN W, YAO P, et al. Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning[J]. Ocean Engineering, 2022, 250: 110947. doi: 10.1016/j.oceaneng.2022.110947
[40]	谢伟, 陶浩, 龚俊斌, 等. 海上无人系统集群发展现状及关键技术研究进展[J]. 中国舰船研究, 2021, 16(1): 7-17, 31.
[41]	赵蕊, 许建, 向先波, 等. 多自主式水下机器人的路径规划和控制技术研究综述[J]. 中国舰船研究, 2018, 13(6): 58-65.
[42]	HASAN M W, ABBAS N H. An adaptive neural network with nonlinear FOPID design of underwater robotic vehicle in the presence of disturbances, uncertainty, and obstacles[J]. Ocean Engineering, 2023, 279: 114451. doi: 10.1016/j.oceaneng.2023.114451
[43]	JI D, ZHOU S, REN J, et al. A prototype of newly dynamic underwater vehicle using fuzzy PID control[C]//2019 IEEE 28th International Symposium on Industrial Electronics(ISIE). Vancouver, Canada: IEEE, 2019: 1121-1126.
[44]	WANG Y, HOU Y, LAI Z, et al. An adaptive PID controller for path following of autonomous underwater vehicle based on Soft Actor-Critic[J]. Ocean Engineering, 2024, 307: 118171. doi: 10.1016/j.oceaneng.2024.118171
[45]	VON BENZON M, SØRENSEN F F, UTH E, et al. An open-source benchmark simulator: Control of a bluerov2 underwater robot[J]. Journal of Marine Science and Engineering, 2022, 10(12): 1898. doi: 10.3390/jmse10121898
[46]	VALAVANIS K P, GRACANIN D, MATIJASEVIC M, et al. Control architectures for autonomous underwater vehicles[J]. IEEE Control Systems Magazine, 1997, 17(6): 48-64. doi: 10.1109/37.642974
[47]	PHILLIPS A B, TEMPLETON R, ROPER D, et al. Autosub long range 1500: A continuous 2000 km field trial[J]. Ocean Engineering, 2023, 280: 114626
[48]	JAFFRE F, LITTLEFIELD R, GRUND M, et al. Development of a new version of the Remus 6000 autonomous underwater vehicle[C]//OCEANS 2019-Marseille. Marseille, France: IEEE, 2019: 1-7.
[49]	GOLDBERG D. Huxley: A flexible robot control architecture for autonomous underwater vehicles[C]//OCEANS 2011 IEEE-Spain. Santander, Spain: IEEE, 2011: 1-10.
[50]	WANG J, TANG Y, LI S, et al. The Haidou-1 hybrid underwater vehicle for the Mariana Trench science exploration to 10, 908 m depth[J]. Journal of Field Robotics, 2024, 41(4): 1054-1079. doi: 10.1002/rob.22307
[51]	XU J, DU Z, HUANG X, et al. Design and development of 10, 000-meter class autonomous underwater vehicle[J]. Journal of Marine Science and Engineering, 2024, 12(11): 2097. doi: 10.3390/jmse12112097
[52]	LIU H, WANG Y, LEWIS F L. Robust distributed formation controller design for a group of unmanned underwater vehicles[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 51(2): 1215-1223.
[53]	赵万龙, 刘功亮, 张敏, 等, 水下多源融合定位与导航技术[M]. 哈尔滨: 哈尔滨工业大学出版社, 2023.
[54]	邢奥成, 李海兵, 阚宝玺, 等. 海洋地磁导航关键技术及发展趋势[J]. 导航定位与授时, 2025, 12(2): 1-14. XING A C, LI H B, KAN B X, et al. Review of key technologies and development trends in marine geomagnetic navigation[J]. Navigation Positioning and Timing, 2025, 12(2): 1-14.
[55]	兰天, 李鼎, 娄琪欣, 等. 水下地形辅助导航算法综述[J]. 导航定位与授时, 2025, 12(1): 14-28.
[56]	ZHANG S, ZHAO S, AN D, et al. Visual SLAM for underwater vehicles: A survey[J]. Computer Science Review, 2022, 46: 100510. doi: 10.1016/j.cosrev.2022.100510
[57]	YANG H, XU Z, JIA B. An underwater positioning system for UUVs based on LiDAR camera and inertial measurement unit[J]. Sensors, 2022, 22(14): 5418. doi: 10.3390/s22145418
[58]	杨健敏, 王佳惠, 乔钢, 等. 水声通信及网络技术综述[J]. 电子与信息学报, 2024, 46(1): 1-21. doi: 10.11999/JEIT230424
[59]	FARR N, WARE J, PONTBRIAND C, et al. Optical communication system expands CORK seafloor observatory's bandwidth[C]//OCEANS 2010 MTS/IEEE SEATTLE. Seattle, Washington, USA: IEEE, 2010: 1-6.
[60]	韩笑天. 水下长距离无线光通信若干关键技术研究 [D]. 西安: 中国科学院大学(中国科学院西安光学精密机械研究所), 2024.
[61]	BOWEN A D, YOERGER D R, TAYLOR C, et al. The Nereus hybrid underwater robotic vehicle for global ocean science operations to 11, 000 m depth[C]//OCEANS 2008. Quebec City, QC, Canada: IEEE, 2008.
[62]	李围, 杨创, 赵胜. 基于CAN总线的全海深锂离子电池组监测系统设计[J]. 船电技术, 2022, 42(10): 80-83. doi: 10.3969/j.issn.1003-4862.2022.10.020
[63]	苑志祥, 张浩, 张雪, 等. 深潜器用蓄电池的研究进展[J]. 硅酸盐学报, 2023, 51(11): 2868-2875.
[64]	KWON L, KANG J G, BAIK K D, et al. Advancement and applications of PEMFC energy systems for large-class unmanned underwater vehicles: A review[J]. International Journal of Hydrogen Energy, 2024, 79: 277-294. doi: 10.1016/j.ijhydene.2024.07.016