海上无人装备关键技术与智能演进展望

金克帆; 王鸿东; 易宏; 刘旌扬; 王健

doi:10.19693/j.issn.1673-3185.01293

海上无人装备关键技术与智能演进展望

doi: 10.19693/j.issn.1673-3185.01293

金克帆^{1, 2,},
王鸿东^{1, 2, ,},
易宏^{1, 2,},
刘旌扬^{1, 2},
王健^{1, 2}

1.
上海交通大学海洋工程国家重点实验室, 上海 200240
2.
上海交通大学海洋智能装备与系统教育部重点实验室, 上海 200240

基金项目:

中国科学院学部咨询评议资助项目 17Z20320037

上海市青年科技英才扬帆计划 18YF1411500

详细信息

作者简介:
金克帆, 男, 1994年生, 博士生。研究方向:海洋智能装备开发。E-mail:jinkefan@sjtu.edu.cn

易宏, 男, 1962年生, 教授, 博士生导师。研究方向:潜器与特种船舶开发, 海上装置与系统开发设计, 系统可靠性与人因工程。E-mail:yihong@sjtu.edu.cn

通信作者:
王鸿东(通信作者), 男, 1989年生, 博士, 讲师。研究方向:船舶系统工程, 海洋智能装备开发。E-mail:whd302@sjtu.edu.cn

中图分类号: U674.941
计量
- 文章访问数: 2323
- HTML全文浏览量: 263
- PDF下载量: 696
- 被引次数: 0
出版历程
- 收稿日期: 2018-05-17
- 网络出版日期: 2018-11-26
- 刊出日期: 2018-12-03

Key technologies and intelligence evolution of maritime UV

JIN Kefan^{1, 2
,},
WANG Hongdong^{1, 2
, ,},
YI Hong^{1, 2
,},
LIU Jingyang^{1, 2},
WANG Jian^{1, 2}

1.
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2.
Key Laboratory of Marine Intelligent Equipment and System, Ministry of Education, Shanghai 200240, China

海上无人装备关键技术与智能演进展望由金克帆，等创作，采用知识共享署名4.0国际许可协议进行许可。

摘要

摘要: 近年来，海上无人装备技术发展迅速，尤其是人工智能技术的发展，使海上无人装备的功能以及性能有了极大的突破。为了明确人工智能技术在海上无人装备应用的研究方向以及演进路线，首先，对国内外海上无人装备技术的发展现状进行综述，随后，分析实现海上无人装备的关键技术。在此基础上对海上无人装备的智能化水平提出一套等级划分标准，并详细定义不同智能等级装备的作业能力以及特点。通过分析，明确了各级之间演进的关键技术。可为海上无人装备的发展路线提供理论基础。
- 无人装备 /
- 人工智能 /
- 智能演进 /
- 无人水面艇 /
- 无人水下机器人
Abstract: In recent years, the technologies of maritime Unmanned Vehicle (UV) have developed rapidly, and especially because the development of artificial intelligence technology, it has made great breakthroughs in the functions and performance of the maritime UV. The development status of the maritime UV technology at home and abroad is reviewed, and then the key technologies for the maritime UV are analyzed. On this basis, a set of classification criteria for the intelligent levels of the maritime UV is proposed, and the operational capabilities and characteristics of the UVs at different intelligent levels are defined in detail, and the key technologies for evolution between levels are clarified. This provides a theoretical basis for the development of the maritime UV.
- Unmanned Vehicle(UV) /
- artificial intelligence /
- intelligence evolution /
- Unmanned Surface Vehicle(USV) /
- Unmanned Underwater Vehicle(UUV)

HTML全文

图 1 “海上猎人”号无人艇

Figure 1. Sea Hunter USV

下载: 全尺寸图片幻灯片

图 2 云洲M80B海底探测无人艇

Figure 2. YUNZHOU M80B USV

下载: 全尺寸图片幻灯片

图 3 “海马”号遥控水下机器人

Figure 3. "HAIMA"-4500 ROV

下载: 全尺寸图片幻灯片

表 1 智能演进说明表

Table 1. Intelligence evolution

序号	智能等级	智能水平	级差优势
1	远程测控级	最优化执行命令，提供辅助信息
2	单机自主级	目标识别，自主航行，自主完成任务	完全独立作业，无需人为操作
3	合作交互级	信息融合，多单位协作交互，编队作业	更广的任务范围，更强的作业能力，更高的可靠性
4	自主学习级	任务过程中自主学习，各单位同步优化	终生学习，持续强化自身能力
5	智能对抗级	形成自身核心价值，迅速适应未知情况，快速学习	迅速理解环境与局势，合理应对未知事件，提供对抗策略

下载: 导出CSV

参考文献(31)

[1]	CAMPBELL S, NAEEMW, IRWIN GW. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres[J]. Annual Reviews in Control, 2012, 36(2):267-283. doi: 10.1016/j.arcontrol.2012.09.008
[2]	American Navy. Unmanned systems intergrated roadmapfy: 2013-2038[R]. USA: Department of Defense, 2013.
[3]	徐玉如, 苏玉民, 庞永杰.海洋空间智能无人运载器技术发展展望[J].中国舰船研究, 2006, 1(3):1-4. doi: 10.3969/j.issn.1673-3185.2006.03.001 XU Y R, SU Y M, PANG Y J. Expectation ofthe development in the technology on ocean space intelligent unmanned vehicles[J]. Chinese Journal of Ship Research, 2006, 1(3):1-4(in Chinese). doi: 10.3969/j.issn.1673-3185.2006.03.001
[4]	American Navy. The navy unmanned undersea vehicle (UUV) master plan[R]. USA: Department of Defense, 2004.
[5]	American Navy. The navy unmanned surface vehicle (USV)master plan[R]. USA: Department of Defense, 2007.
[6]	万接喜.外军无人水面艇发展现状与趋势[J].国防科技, 2014, 35(5):91-96. http://d.old.wanfangdata.com.cn/Periodical/gfkj201405021 WAN J X. Status and development trends of foreign military unmanned surface boats[J]. National Defense Science & Technology, 2014, 35(5):91-96(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/gfkj201405021
[7]	李家良.无人水面艇发展与应用[J].火力与指挥控制, 2012, 37(6):203-207. doi: 10.3969/j.issn.1002-0640.2012.06.053 LI J L. Development and application of unmanned surface vehicle[J]. Fire Control & Command Control, 2012, 37(6):203-207(in Chinese). doi: 10.3969/j.issn.1002-0640.2012.06.053
[8]	熊亚洲, 张晓杰, 冯海涛, 等.一种面向多任务应用的无人水面艇[J].船舶工程, 2012, 34(1):16-19. doi: 10.3969/j.issn.1000-6982.2012.01.005 XIONG Y Z, ZHANG X J, FENG H T, et al. An unmanned surface vehicle for multi-mission applications[J]. Ship Engineering, 2012, 34(1):16-19(in Chinese). doi: 10.3969/j.issn.1000-6982.2012.01.005
[9]	徐玉如, 李彭超.水下机器人发展趋势[J].自然杂志, 2011, 33(3):125-132. http://d.old.wanfangdata.com.cn/Periodical/zrzz201103001 XU Y R, LI P C. Developing tendency of unmanned underwater vehicles[J]. Chinese Journal of Nature, 2011, 33(3):125-132(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/zrzz201103001
[10]	潘光, 宋保维, 黄桥高, 等.水下无人系统发展现状及其关键技术[J].水下无人系统学报, 2017, 25(2):44-51. http://www.cnki.com.cn/Article/CJFDTOTAL-YLJS201702005.htm PAN G, SONG B W, HUANG Q G, et al. Development and key techniques of unmanned undersea system[J]. Journal of Unmanned Undersea Systems, 2017, 25(2):44-51(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-YLJS201702005.htm
[11]	连琏, 马厦飞, 陶军."海马"号4500米级ROV系统研发历程[J].船舶与海洋工程, 2015, 31(1):9-12. doi: 10.3969/j.issn.2095-4069.2015.01.003 LIAN L, MA X F, TAO J. R & D process of 4500 m ROV system"Hai Ma"[J]. Naval Architecture and Ocean Engineering, 2015, 31(1):9-12(in Chinese). doi: 10.3969/j.issn.2095-4069.2015.01.003
[12]	易宏, 梁晓锋.泛可靠性工程理论与实践[M].上海:上海交通大学出版社, 2017.
[13]	刁海龙.反水雷、反潜、反导和反恐的利剑——无人水面艇系统[J].水雷战与舰船防护, 2011, 19(1):1-5. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201102959051 DIAO H L. Unmanned surface vehicle system the sword of MCM, ASW, anti-missile and anti-terrorism[J]. Mine Warfare & Ship Self-Defence, 2011, 19(1):1-5(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201102959051
[14]	陈波, 金瓯, 涂娟.海上超短波通信距离分析[J].舰船科学技术, 2010, 32(6):88-90, 127. http://d.old.wanfangdata.com.cn/Periodical/jckxjs201006020 CHEN B, JIN O, TU J. Analysis of marine ultra-short wave communication distance[J]. Ship Science and Technology, 2010, 32(6):88-90, 127(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jckxjs201006020
[15]	钱东, 唐献平, 赵江. UUV技术发展与系统设计综述[J].鱼雷技术, 2014, 22(6):401-414, 419. doi: 10.3969/j.issn.1673-1948.2014.06.001 QIAN D, TANG X P, ZHAO J. Overview of technology development and system design of UUVs[J]. Torpedo Technology, 2014, 22(6):401-414, 419(in Chinese). doi: 10.3969/j.issn.1673-1948.2014.06.001
[16]	许志杰, 王晶, 刘颖.计算机视觉核心技术现状与展望[J].西安邮电学院学报, 2012, 17(6):1-8. http://d.old.wanfangdata.com.cn/Periodical/xaydxyxb201206002 XU Z J, WANG J, LIU Y. A review of computer vision development and trends[J]. Journal of Xi'an University of Posts and Telecommunications, 2014, 17(6):1-8(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/xaydxyxb201206002
[17]	SERMANET P, EIGEN D, ZHANG X, et al. OverFeat: integrated recognition, localization and detection using convolutional networks[J]. arXivpreprint arXiv: 1312.6229, 2013.
[18]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014, 2: 2672-2680.
[19]	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXivpreprint arXiv: 1409.1556, 2014.
[20]	JOHNSON J, HARIHARAN B, VAN DER MAATEN L, et al. Inferring and executing programs for visual reasoning[J]. arXivpreprint arXiv: 1705.03633, 2017: 3008-3017.
[21]	SANTORO A, RAPOSO D, BARRETT D G T, et al. A simple neural network module for relational reasoning[J]. arXivpreprint arXiv: 1706.01427, 2017.
[22]	SUTTON R S, BARTO A G. Reinforcement learning:an introduction[M].Cambridge:MIT Press, 1998:216-224.
[23]	LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[J]. arXivpreprint arXiv: 1509.02971, 2015.
[24]	MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[J]. arXivpreprint arXiv: 1312.5602, 2013.
[25]	HO J, ERMON S. Generative adversarial imitation learning[J]. arXivpreprint arXiv: 1606.03476, 2016.
[26]	RUSU A A, VECERIK M, ROTHÖRL T, et al. Sim-to-real robot learning from pixels with progressive nets[J]. arXivpreprint arXiv: 1610.04286, 2016.
[27]	TAYLOR M E, STONE P. Transfer learning for reinforcement learning domains:a survey[J]. Journal of Machine Learning Research, 2009, 10:1633-1685. http://dl.acm.org/citation.cfm?id=1755839
[28]	FINN C, YU T H, ZHANG T H, et al. One-shot visual imitation learning via meta-learning[J]. arXivpreprint arXiv: 1709.04905, 2017.
[29]	马忠丽, 文杰, 梁秀梅, 等.无人艇视觉系统多类水面目标特征提取与识别[J].西安交通大学学报, 2014, 48(8):60-66. http://d.old.wanfangdata.com.cn/Periodical/xajtdxxb201408011 MA Z L, WEN J, LIANG X M, et al. Extraction and recognition of features from multi-types of surface targets for visual systems in unmanned surface vehicle[J]. Journal of Xi'an JiaoTong University, 2014, 48(8):60-66.(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/xajtdxxb201408011
[30]	齐小伟, 任光.基于领导跟随的船舶航迹控制[J].船舶, 2016, 27(1):92-99. http://d.old.wanfangdata.com.cn/Periodical/cb201601015 QI X W, REN G. Ship track control based on leader-follower[J]. Ship & Boat, 2016, 27(1):92-99(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/cb201601015
[31]	LILLICRAPT P, HUNT J J, PRITZEL, A, et al. Continuous control with deep reinforcement learning[J]. arXivpreprint arXiv: 1509.02971, 2015.