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机器人在管道提升式深海采矿系统中智能化的挑战与发展趋势

  • 沈义俊 ,
  • 张炜峰 ,
  • 周健一 ,
  • 全嘉鑫 ,
  • 李文庆 ,
  • 刘悦凡 ,
  • 张瑞永 ,
  • 李萌
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  • 1. 海南大学南海海洋资源利用国家重点实验室, 海口 570228;
    2. 海南大学海洋科学与工程学院, 海口 570228
沈义俊,教授,研究方向为海洋能源开发及深海工程,电子信箱:sheny2000@hainanu.edu.cn

收稿日期: 2023-09-28

  修回日期: 2023-10-31

  网络出版日期: 2024-07-09

Challenges and development trend of intelligent robot in pipe lifting deep sea mining system

  • SHEN Yijun ,
  • ZHANG Weifeng ,
  • ZHOU Jianyi ,
  • QUAN Jiaxin ,
  • LI Wenqing ,
  • LIU Yuefan ,
  • ZHANG Ruiyong ,
  • LI Meng
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  • 1. State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou 570228, China;
    2. School of Marine Science and Engineering, Hainan University, Haikou 570228, China

Received date: 2023-09-28

  Revised date: 2023-10-31

  Online published: 2024-07-09

摘要

近年来对深海矿物资源的开采需求逐步增大,为了解决传统的管道提升式采矿系统的缺陷,提出将智能化机器人引入来实现高效、精准的矿物采集和结构物的健康监测。但由于深海环境复杂,与陆地上机器人相比,水下机器人的设计需要考虑海洋带来的阻力、噪声等多方面的影响。从水动力学、水下定位和水下视觉3个方面介绍了管道提升式深海采矿系统中采矿机器人的发展与技术难点,概述了机器人管道检测技术。探讨了深海资源开采水下机器人的技术发展方向。

本文引用格式

沈义俊 , 张炜峰 , 周健一 , 全嘉鑫 , 李文庆 , 刘悦凡 , 张瑞永 , 李萌 . 机器人在管道提升式深海采矿系统中智能化的挑战与发展趋势[J]. 科技导报, 2024 , 42(12) : 92 -106 . DOI: 10.3981/j.issn.1000-7857.2023.09.01377

Abstract

In recent years, the demand for deep-sea mineral resources has gradually increased. In order to solve the shortcomings of traditional pipe lifting mining system, intelligent robots are proposed to realize efficient and accurate mineral collection and health monitoring of the structure. However, due to the complexity of the deep-sea environment, compared with robots on land, the design of underwater robots needs to take into account the effects of resistance, noise, and other aspects brought by the ocean. This paper introduces the development and technical difficulties of mining robots in pipe-lifting deep-sea mining systems from three aspects: hydrodynamics, underwater localization, and underwater vision. Meanwhile, it provides an overview of robotic pipeline inspection technology. On this basis, this paper discusses the technological development direction of underwater robots for deep-sea resource mining.

参考文献

[1] 沈义俊, 陈敏芳, 杜燕连, 等. 深海矿物资源开发系统关键力学问题及技术挑战[J]. 力学与实践, 2022, 44(5):1005-1020.
[2] Skålvik A M, Saetre C, Frøysa K E, et al. Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors[J]. Frontiers in Marine Science, 2023, 10:1152236.
[3] Sartore C, Campos R, Quintana J, et al. Control and perception framework for deep sea mining exploration[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ:IEEE, 2019:6348-6353.
[4] Sun K, Cui W C, Chen C. Review of underwater sensing technologies and applications[J]. Sensors, 2021, 21(23):7849.
[5] Liu C H, Guo J J, Tian Y, et al. Development and field tests of a deep-sea laser-induced breakdown spectroscopy (LIBS) system for solid sample analysis in seawater[J]. Sensors, 2020, 20(24):7341.
[6] Leng D X, Shao S, Xie Y C, et al. A brief review of recent progress on deep sea mining vehicle[J]. Ocean Engineering, 2021, 228:108565.
[7] Ben-Ari M, Mondada F. Elements of Robotics[M]. Cham:Springer International Publishing, 2018.
[8] 薛乃耀. 作业型水下机器人运动控制系统研究[D]. 广州:华南理工大学, 2020.
[9] Fossen T I. Handbook of marine craft hydrodynamics and motion control[M]. New York:Wiley, 2011.
[10] Eidsvik O A, Schjølberg I. Time domain modeling of rov umbilical using beam equations[J]. IFAC-PapersOnLine, 2016, 49(23):452-457.
[11] 习刚, 杨兴满, 陈卫东. ROV同步航行水下缆索运动仿真[J]. 舰船科学技术, 2011, 33(5):24-27.
[12] Huo X X, Ge T, Wang X Y. Horizontal path-following control for deep-sea work-class ROVs based on a fuzzy logic system[J]. Ships and Offshore Structures, 2018, 13(6):637-648.
[13] 郑男. 基于深度特征的海底矿物图像分割算法研究[D]. 北京:中央民族大学, 2019.
[14] Zhou H Y, Jiao P C, Lin Y. Emerging deep-sea smart composites:Advent, performance, and future trends[J]. Materials, 2022, 15(18):6469.
[15] 戴瑜. 履带式集矿机海底行走的单刚体建模研究与仿真分析[D]. 长沙:中南大学, 2010.
[16] 阳宁, 陈光国. 深海矿产资源开采技术的现状综述[J]. 矿山机械, 2010, 38(10):4-9.
[17] Welling C G. An advanced design deep sea mining system[C]//Proceedings of All Days. OTC, 1981.
[18] Liu S J, Yang N, Han Q J. Research and development of deep sea mining technology in China[C]//Proceedings of ASME 201029th International Conference on Ocean, Offshore and Arctic Engineering, 2010:163-169.
[19] Halkyard J. Technology for mining cobalt rich manganese crusts from seamounts[C]//Proceedings of OCEANS '85-Ocean Engineering and the Environment. Piscataway, NJ:IEEE, 1985:352-374.
[20] 夏毅敏. 深海钴结壳螺旋切削采集过程仿真和螺旋采集头工作参数优化研究[D]. 长沙:中南大学, 2006.
[21] Skarpelis N, Argyraki A. Geology and origin of supergene ore at the lavrion Pb-Ag-Zn deposit, Attica, Greece[J]. Resource Geology, 2009, 59(1):1-14.
[22] Crowhurst P, Lowe J. Exploration and resource drilling of seafloor massive sulfide (SMS) deposits in the Bismarck Sea, Papua New Guinea[C]//Proceedings of OCEANS'11 MTS/IEEE KONA. Piscataway, NJ:IEEE, 2011:1-6.
[23] Vladimirs R, Konstantins S. Smarthub for supervising system for resource exploration and pollution control in deep-water and coastal areas based on ICT technologies[J]. Marine Economics and Management, 2023, 6(1):23-34.
[24] Yang G Z, Bellingham J, Dupont P E, et al. The grand challenges of Science Robotics[J]. Science Robotics, 2018, 3(14):eaar7650.
[25] Teague J, Allen M J, Scott T B. The potential of lowcost ROV for use in deep-sea mineral, ore prospecting and monitoring[J]. Ocean Engineering, 2018, 147:333-339.
[26] Parra Rubio A, Fan D X, Jenett B, et al. Modular morphing lattices for large-scale underwater continuum robotic structures[J]. Soft Robotics, 2023, 10(4):724-736.
[27] Nain M, Goyal N. Localization techniques in underwater wireless sensor network[C]//Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Piscataway, NJ:IEEE, 2021:747-751.
[28] Sun D J, Zheng C E, Cui H Y, et al. Developing status and some cutting-edge issues of underwater sensor network localization technology[J]. Scientia Sinica Informationis, 2018, 48(9):1121-1136.
[29] Qiao G, Babar Z, Ma L, et al. MIMO-OFDM underwater acoustic communication systems:A review[J]. Physical Communication, 2017, 23:56-64.
[30] Sahoo A, Dwivedy S K, Robi P S. Advancements in the field of autonomous underwater vehicle[J]. Ocean Engineering, 2019, 181:145-160.
[31] 李守军, 包更生, 吴水根. 水声定位技术的发展现状与展望[J]. 海洋技术, 2005(1):130-135.
[32] 石桂欣, 鄢社锋, 吴永清. 一种基于纯方位的虚拟长基线定位算法[C]//中国声学学会2017年全国声学学术会议论文集. 北京:中国声学学会, 2017:389-390.
[33] 张涛, 夏茂栋, 张佳宇, 等. 水下导航定位技术综述[J]. 全球定位系统, 2022, 47(4):1-16.
[34] 邸凯昌, 万文辉, 赵红颖, 等. 视觉SLAM技术的进展与应用[J]. 测绘学报, 2018, 47(6):770-779.
[35] Dong L L, Zhang W D, Xu W H. Underwater image enhancement via integrated RGB and LAB color models[J]. Signal Processing:Image Communication, 2022, 104:116684.
[36] Cherian A K, Poovammal E, Philip N S, et al. Deep learning based filtering algorithm for noise removal in underwater images[J]. Water, 2021, 13(19):2742.
[37] Fu X Y, Cao X Y. Underwater image enhancement with global-local networks and compressed-histogram equalization[J]. Signal Processing:Image Communication, 2020, 86:115892.
[38] Han P L, Liu F, Yang K, et al. Active underwater descattering and image recovery[J]. Applied Optics, 2017, 56(23):6631-6638.
[39] Li C, Guo C, Ren W, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE transactions on image processing, 2019, 29:4376-4389.
[40] Zhang W D, Jin S L, Zhuang P X, et al. Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement[J]. IEEE Signal Processing Letters, 2023, 30:229-233.
[41] Raveendran S, Patil M D, Birajdar G K. Underwater image enhancement:A comprehensive review, recent trends, challenges and applications[J]. Artificial Intelligence Review, 2021, 54(7):5413-5467.
[42] Kerstens R, Laurijssen D, Schouten G, et al. 3D point cloud data acquisition using a synchronized In-air imaging sonar sensor network[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ:IEEE, 2019:5855-5861.
[43] Kim B, Cho H, Joe H, et al. Optimal strategy for seabed 3D mapping of AUV based on imaging sonar[C]//Proceedings of OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO). Piscataway, NJ:IEEE, 2018:1-5.
[44] Li S Y, Su D P, Yang F L, et al. Bathymetric LiDAR and multibeam echo-sounding data registration methodology employing a point cloud model[J]. Applied Ocean Research, 2022, 123:103147.
[45] Xu W X, Zhang F, Jiang T, et al. Feature curve-based registration for airborne LiDAR bathymetry point clouds[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112:102883.
[46] Huang T X, Liu Y. 3D point cloud geometry compression on deep learning[C]//Proceedings of the 27th ACM International Conference on Multimedia. New York:ACM, 2019:890-898.
[47] Palomer A, Ridao P, Ribas D. Inspection of an underwater structure using point-cloud SLAM with an AUV and a laser scanner[J]. Journal of Field Robotics, 2019, 36(8):1333-1344.
[48] Li X, Xue F, Chen C, et al. Graph attention-based deep neural network for 3D point cloud processing[C]//Proceedings of IEEE International Conference on Multimedia and Expo (ICME). Piscataway, NJ:IEEE, 2021:1-6.
[49] Wang X X, Gao J, Feng L. Recognition and 3D pose estimation for underwater objects using deep convolutional neural network and point cloud registration[C]//Proceedings of International Conference on System Science and Engineering (ICSSE). Piscataway, NJ:IEEE, 2020:1-6.
[50] Afham M, Dissanayake I, Dissanayake D, et al. CrossPoint:Self-supervised cross-modal contrastive learning for 3D point cloud understanding[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ:IEEE, 2022:9892-9902.
[51] Ambati P, Raj K M, Joshuva A. A review on pipeline inspection robot[C]//AIP Conference Proceedings. Chennai:AIP Publishing, 2020, 2311:60002.
[52] Zhao X, Wang X, Du Z. Research on detection method for the leakage of underwater pipeline by YOLOv3[C]//2020 IEEE international conference on mechatronics and automation (ICMA). Beijing:IEEE, 2020:637-642.
[53] Pasha M A, Khan T M. A pipeline inspection gauge based on low cost magnetic flux leakage sensing magnetometers for non-destructive testing of pipelines[C]//Proceedings of International Conference on Emerging Technologies (ICET). Piscataway, NJ:IEEE, 2016:1-5.
[54] Jin T, Que P W, Tao Z S. Development of magnetic flux leakage pipe inspection robot using hall sensors[C]//Proceedings of Micro-Nanomechatronics and Human Science, 2004 and The Fourth Symposium Micro-Nanomechatronics for Information-Based Society, 2004. Piscataway, NJ:IEEE, 2004:325-329.
[55] Safizadeh M, Hasanian M. Gas pipeline corrosion mapping using pulsed eddy current technique[J]. International Journal of Advanced Design and Manufacturing Technology, 2011, 5(1):11-18.
[56] Mazreah A A, Alnaimi F B I, Sahari K S M. Novel design for PIG to eliminate the effect of hydraulic transients in oil and gas pipelines[J]. Journal of Petroleum Science and Engineering, 2017, 156:250-257.
[57] Kondratiev S I, Dantsevich I M, Tarasenko A A. Pipeline monitoring technology in Nord Stream 2[J]. IOP Conference Series:Earth and Environmental Science, 2021, 872(1):012021.
[58] Liljeback P, Mills R. Eelume:A flexible and subsea resident IMR vehicle[C]//Proceedings of OCEANS 2017-Aberdeen. Piscataway, NJ:IEEE, 2017:1-4.
[59] Jawhar I, Mohamed N, Al-Jaroodi J, et al. An architecture for using autonomous underwater vehicles in wireless sensor networks for underwater pipeline monitoring[J]. IEEE Transactions on Industrial Informatics, 2019, 15(3):1329-1340.
[60] Gothi A, Patel P, Pandya M. Underwater robotics[M]//ICT with Intelligent Applications. Singapore:Springer Singapore, 2021:445-453.
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