海上无人系统集群发展现状及关键技术研究进展

谢伟; 陶浩; 龚俊斌; 罗威; 尹逢川; 梁晓龙

doi:10.19693/j.issn.1673-3185.02225

海上无人系统集群发展现状及关键技术研究进展

doi: 10.19693/j.issn.1673-3185.02225

1.
中国舰船研究设计中心，湖北武汉 430064
2.
空军工程大学空管领航学院，陕西西安 710051

基金项目: 国家自然科学基金资助项目(61701471)

详细信息

作者简介:
谢伟，男，1969年生，博士，研究员

陶浩，男，1987年生，博士，工程师

龚俊斌，男，1978年生，博士，高级工程师

通信作者:
陶浩

中图分类号: U674.77; V279
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- 被引次数: 0
出版历程
- 收稿日期: 2020-12-16
- 修回日期: 2021-01-07
- 网络出版日期: 2021-02-05
- 刊出日期: 2021-02-28

Research advances in the development status and key technology of unmanned marine vehicle swarm operation

1.
China Ship Development and Design Center, Wuhan 430064, China
2.
Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China

海上无人系统集群发展现状及关键技术研究进展由谢伟，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: 海上无人系统集群作战正在从概念走向实装应用。着眼于海上无人系统集群作战任务的需要，总结无人机集群、水面无人艇集群、无人水下机器人集群和跨域无人系统集群的国内外发展现状，分析海上无人系统集群协同作战所需的关键技术，包括通信自组网、协同态势感知、任务分配、航迹规划、集群编队控制和虚拟测试等。系统性归纳海上无人系统集群所需各项技术的主要研究思路、代表性算法及相关算法的研究趋势，期望能够为海上无人系统集群技术研究提供有益的参考和借鉴。
- 海上无人系统集群 /
- 跨域协同 /
- 关键技术 /
- 综述
Abstract: The swarm operation of unmanned marine vehicle (UMV) has been developed and evolved from concept to practical application. In view of the mission requirements of UMV swarms, this paper summarizes the development of the UMV swarm concepts of unmanned aerial vehicle (UAV), unmanned surface vehicle (USV) and unmanned underwater vehicle (UUV), and cross-domain swarm operations of UMV in naval warfare. It then analyzes the key technologies of UMV swarm cooperative engagement, including self-organizing communication, collaborative situational awareness, tasking, path planning, formation control and virtual testing. Finally, the main research ideas, representative algorithms and development trends of the related algorithms are summarized systematically. This study can provide valuable references for research on UMV swarm operation technology.
- swarm of unmanned marine vehicle /
- cross-domain collaboration /
- key technology /
- review

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图 1 分布式任务分配模型及方法

Figure 1. Distributed tasking model and method

下载: 全尺寸图片幻灯片

表 1 国外海上跨域无人系统集群典型项目

Table 1. Typical demonstration projects of cross-domain swarm operations of marine unmanned vehicle

项目主题	承担机构	开展时间/年	演示验证内容	平台类型及数量
异构无人系统跨域通信	美国通用动力公司	2016	UUV，UAV，核潜艇间的跨域通信	1艘UUV、1架UAV、1艘核潜艇
		2017	在2016年实现跨域通信的基础上，验证由UUV发射UAV	1艘“金枪鱼-21”UUV、1架“黑翼”UAV
		2019	USV，UUV，濒海战斗舰(LCS)以及核潜艇等有人无人作战平台跨域协同通信、探测信息传输验证	“金枪鱼-9”UUV，通用USV，LCS和核潜艇
	美国航空环境公司	2016	UAV由核潜艇发射，作为核潜艇，UUV，USV间的通信中继	1架“黑翼”UAV、1艘核潜艇、1艘UUV、1艘有人水面舰艇
	美国洛克希德·马丁公司	2016	UUV发射UAV，“矢量鹰”固定翼UAV、“金枪鱼”UUV、核潜艇跨域通信	1艘USV、1艘UUV
	美国波音公司	2017	UUV与USV间的跨域协同通信	1艘USV、1艘UUV
	美国海德罗伊公司	2017	UUV与UAV协同执行ISR任务	1架“黑翼”UAV、1艘REMUS 600 UUV
协同指挥	美国诺斯罗普·格鲁曼公司	2016	开发全新的跨域异构无人系统协同作战控制架构“先进任务管理与控制系统”（AMMCS）	1艘REMUS 600 UUV、2艘“波浪滑翔者”USV和1架有人直升机
	美国诺斯罗普·格鲁曼公司	2017	开发“自主控制、发展和认知”（ACER）系统，实现了单系统对多个UAV，USV和UUV的指挥控制	1艘“普罗特斯”大型UUV、1艘REMUS 100 UUV、1艘IVER UUV、2艘“激流”UUV，2艘“波浪滑翔者”USV和1架UAV
	英国奎奈蒂克公司	2016	开发ACER系统，实现单系统对多个UAV，USV和UUV的指挥控制	25种无人系统:UAV，USV和UUV
	法国舰艇建造局	2017	利用I4^®Drones任务系统成功实现3种无人系统协同探测、识别、拦截和摧毁敌小艇的指控作战演示	IT180小型旋翼UAV，REMORINA USV和UUV

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参考文献(67)

[1]	邹立岩, 张明智, 荣明. 智能无人机集群概念及主要发展趋势分析[J]. 战术导弹技术, 2019(5): 1–11, 43. ZOU L Y, ZHANG M Z, RONG M. Analysis of intelligent unmanned aircraft systems swarm concept and main development trend[J]. Tactical Missile Technology, 2019(5): 1–11, 43 (in Chinese).
[2]	董晓明. 海上无人装备体系概览[M]. 哈尔滨: 哈尔滨工程大学出版社, 2020. DONG X M. Introduction to martime unmanned systems[M]. Harbin: Harbin Engineering University Press, 2020 (in Chinese).
[3]	张伟, 王乃新, 魏世琳, 等. 水下无人潜航器集群发展现状及关键技术综述[J]. 哈尔滨工程大学学报, 2020, 41(2): 289–297. ZHANG W, WANG N X, WEI S L, et al. Overview of unmanned underwater vehicle swarm development status and key technologies[J]. Journal of Harbin Engineering University, 2020, 41(2): 289–297 (in Chinese).
[4]	王宇, 郭兴旺. 无人系统集群海上作战应用研究[J]. 舰船电子工程, 2019, 39(12): 21–25. WANG Y, GUO X W. Research on the application of unmanned system cluster in marine combat applications[J]. Ship Electronic Engineering, 2019, 39(12): 21–25 (in Chinese).
[5]	SHANEN SHAH A F M, ILHAN H, TURELI U. CB-MAC: a novel cluster-based MAC protocol for VANETs[J]. IET Intelligent Transport Systems, 2019, 13(4): 587–595. doi: 10.1049/iet-its.2018.5267
[6]	JAVADI M, MOSTAFAEI H, CHOWDHURRY M U, et al. Learning automaton based topology control protocol for extending wireless sensor networks lifetime[J/OL]. Journal of Network and Computer Applications, 2018, 122: 128–136.
[7]	XU W, DANESHMAND, LIU W. A data privacy protective mechanism for WBAN[J/OL]. Wireless Communications and Mobile Computing, 2015: 421–430. https://doi.org/10.1002/wcm.
[8]	GUI J S, ZHOU K. Flexible adjustments between energy and capacity for topology control in heterogeneous wireless multi-hop networks[J]. Journal of Network and Systems Management, 2016, 24(4): 789–812. doi: 10.1007/s10922-016-9367-y
[9]	WAQAS A, MAHMOOD H, ZARIFZADEH S. A data privacy protective mechanism for WBAN[J]. Computer Networks, 2019, 3819(4): 24–30.
[10]	HAYAT S, YANMAZ E, MUZAFFAR R. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4): 2624–2661.
[11]	何友, 姚力波. 天基海洋目标信息感知与融合技术研究[J]. 武汉大学学报·信息科学版, 2017, 42(11): 1530–1536. HE Y, YAO L B. Space-based sea target information awareness and fusion[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11): 1530–1536 (in Chinese).
[12]	高杨, 李东生, 程泽新. 无人机分布式集群态势感知模型研究[J]. 电子与信息学报, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877 GAO Y, LI D S, CHENG Z X. UAV distributed swarm situation awareness model[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1271–1278 (in Chinese). doi: 10.11999/JEIT170877
[13]	崔玮. 改进的D-S证据理论在海上目标融合识别模型中的应用[J]. 舰船科学技术, 2017, 39(8): 109–111. CUI W. The application of improved D-S proof theory in the model of sea target fusion recognition[J]. Ship Science and Technology, 2017, 39(8): 109–111 (in Chinese).
[14]	张悦. 多USV协同定位数据融合技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2018. ZHANG Y. Research on multi USV cooperative positioning data fusion technology[D]. Harbin: Harbin University of Technology, 2018 (in Chinese).
[15]	WANG Q Y, CUI X F, LI Y B, et al. Performance enhancement of a USV INS/CNS/DVL integration navigation system based on an adaptive information sharing factor federated filter[J]. Sensors, 2017, 17(2): 239. doi: 10.3390/s17020239
[16]	XIAO F Y. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy[J]. Information Fusion, 2019, 46: 23–32. doi: 10.1016/j.inffus.2018.04.003
[17]	荣健, 乔文钊. 基于模糊神经系统的多传感器数据融合算法[J]. 电子科技大学学报, 2010, 39(3): 376–378, 424. doi: 10.3969/j.issn.1001-0548.2010.03.011 RONG J, QIAO W Z. Neural-fuzzy-based multisensor data fusion architecture[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(3): 376–378, 424 (in Chinese). doi: 10.3969/j.issn.1001-0548.2010.03.011
[18]	张雨浓, 杨逸文, 肖秀春, 等. 样条神经网络的权值直接确定法[J]. 系统工程与电子技术, 2009, 31(11): 2685–2688. ZHANG Y N, YANG Y W, XIAO X C, et al. Weights direct determination of a spline neural network[J]. Systems Engineering and Electronics, 2009, 31(11): 2685–2688 (in Chinese).
[19]	郭继峰, 郑红星, 贾涛, 等. 异构无人系统协同作战关键技术综述[J]. 宇航学报, 2020, 41(6): 686–696. GUO J F, ZHENG H X, JIA T, et al. Summary of key technologies for heterogeneous unmanned system cooperative operations[J]. Journal of Astronautics, 2020, 41(6): 686–696 (in Chinese).
[20]	AN S, KIM H J. Simultaneous task assignment and path planning using mixed-integer linear programming and potential field method[C]//2013 13th International Conference on Control, Automation and Systems (ICCAS 2013). Gwangju, South Korea: IEEE, 2014.
[21]	ZHU D Q, HUANG H, YANG S X. Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace[J]. IEEE Transactions on Cybernetics, 2013, 43(2): 504–514. doi: 10.1109/TSMCB.2012.2210212
[22]	LEVCHUK G M, LEVCHUK Y N, MEIRINA C, et al. Normative design of project-based organizations-part III: modeling congruent, robust, and adaptive organizations[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2004, 34(3): 337–350. doi: 10.1109/TSMCA.2003.822268
[23]	HOANG P D, RABAEY J M. Scheduling of DSP programs onto multiprocessors for maximum throughput[J]. IEEE Transactions on Signal Processing, 1993, 41(6): 2225–2235. doi: 10.1109/78.218149
[24]	刘华罡, 方浩, 毛昱天, 等. 多智能体系统分布式群集运动与避障控制[C]//第二十九届中国控制会议论文集. 北京: 中国自动化学会, 2010: 4536-4541. LIU H G, FANG H, MAO Y T, et al. Distributed flocking control and obstacle avoidance for multi-agent systems[C]//Proceedings of the 29th Chinese Control Conference. Beijing: Chinese Association of Automation, 2010: 4536-4541 (in Chinese).
[25]	王训, 王兆魁, 张育林. 基于合作博弈的智能集群自主聚集策略[J]. 国防科技大学学报, 2017, 39(2): 146–151. doi: 10.11887/j.cn.201702022 WANG X, WANG Z K, ZHANG Y L. Strategy about autonomous aggregation of intelligent swarm based on cooperative game[J]. Journal of national university of defense technology, 2017, 39(2): 146–151 (in Chinese). doi: 10.11887/j.cn.201702022
[26]	HAKSAR R N, SCHWAGER M. Distributed deep reinforcement learning for fighting forest fires with a network of aerial robots[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018.
[27]	HU J, XIE L, LUM K Y, et al. Multiagent information fusion and cooperative control in target search[J]. IEEE Transactions on Control Systems Technology, 2013, 21(4): 1223–1235. doi: 10.1109/TCST.2012.2198650
[28]	JIN Y N, WU Y X, FAN N J. Research on distributed cooperative control of swarm UAVs for persistent coverage[C]//Proceedings of the 33rd Chinese Control Conference. Nanjing, China: IEEE, 2014.
[29]	SMITH R G. The contract net protocol: high-level communication and control in a distributed problem solver[J]. IEEE Transactions on Computers, 1980, C-29(12): 1104–1113. doi: 10.1109/TC.1980.1675516
[30]	FU X W, FENG P, GAO X G. Swarm UAVs task and resource dynamic assignment algorithm based on task sequence mechanism[J]. IEEE Access, 2019, 7: 41090–41100. doi: 10.1109/ACCESS.2019.2907544
[31]	HUNT S, MENG Q, HINDE C J. An extension of the consensus-based bundle algorithm for multi-agent tasks with task based requirements[C]//2012 11th International Conference on Machine Learning and Applications. Boca Raton, FL, USA: IEEE, 2013.
[32]	SHANMUGAVEL M, TSOURDOS A, WHITE B, et al. Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs[J]. Control Engineering Practice, 2010, 18(9): 1084–1092. doi: 10.1016/j.conengprac.2009.02.010
[33]	LIU J, HE Y F, ZHANG A P. An improved mutli-ACS algorithm for the waste collection vehicle arc routing problem with turn constraints[C]//2014 Tenth International Conference on Computational Intelligence and Security. Kunming, China: IEEE, 2015.
[34]	ARI I, AKSAKALLI V, AYDOĞDU V, et al. Optimal ship navigation with safety distance and realistic turn constraints[J]. European Journal of Operational Research, 2013, 229(3): 707–717. doi: 10.1016/j.ejor.2013.03.022
[35]	KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research, 1986, 5(1): 90–98. doi: 10.1177/027836498600500106
[36]	EDISON E, SHIMA T. Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 2011, 38(1): 340–356.
[37]	NIKOLOS I K, VALAVANIS K P, TSOURVELOUDIS N C, et al. Evolutionary algorithm based offline/online path planner for UAV navigation[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2003, 33(6): 898–912. doi: 10.1109/TSMCB.2002.804370
[38]	BRINTAKI A N, NIKOLOS I K. Coordinated UAV path planning using differential evolution[J]. Operational Research, 2005, 5(3): 487–502. doi: 10.1007/BF02941133
[39]	MITTAL S, DEB K. Three-dimensional offline path planning for UAVs using multiobjective evolutionary algorithms[C]//2007 IEEE Congress on Evolutionary Computation. Singapore: IEEE, 2007.
[40]	ÖZALP N, SAHINGOZ O K. Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms[C]//2013 International Conference on Unmanned Aircraft Systems (ICUAS). Atlanta, GA, USA: IEEE, 2013: 49-57.
[41]	SHEN X N. A quantum evolutionary algorithm for robot path planning in dynamic environment[C]//Proceedings of the 32nd Chinese Control Conference. Xi'an, China: IEEE, 2013.
[42]	CHENG Z, SUN Y, LIU Y L. Path planning based on immune genetic algorithm for UAV[C]//2011 International Conference on Electric Information and Control Engineering. Wuhan, China: IEEE, 2011.
[43]	HASIRCIOGLU I, TOPCUOGLU H R, ERMIS M. 3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms[C]//Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. Atlanta, GA, USA: ACM, 2008.
[44]	AL-SABBAN W H, GONZALEZ L F, SMITH R N. Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes[C]//2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013.
[45]	RAGI S, CHONG E K P. UAV path planning in a dynamic environment via partially observable Markov decision process[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2397–2412. doi: 10.1109/TAES.2013.6621824
[46]	FEYZABADI S, CARPIN S. Risk-aware path planning using hirerachical constrained markov decision processes[C]//2014 IEEE International Conference on Automation Science and Engineering (CASE). Taipei, Taiwan, China: IEEE, 2014.
[47]	ZHANG B C, MAO Z L, LIU W Q, et al. Geometric reinforcement learning for path planning of UAVs[J]. Journal of Intelligent & Robotic Systems, 2015, 77(2): 391–409.
[48]	YANG H Y, GUO Q, XU X, et al. Self-learning PD algorithms based on approximate dynamic programming for robot motion planning[C]//2014 International Joint Conference on Neural Networks (IJCNN). Beijing, China: IEEE, 2014.
[49]	KONAR A, CHAKRABORTY I G, SINGH S J, et al. A deterministic improved Q-learning for path planning of a mobile robot[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43(5): 1141–1153. doi: 10.1109/TSMCA.2012.2227719
[50]	ASL S V, DAVARZANI Z, STAJI S. Planning flying robot navigation in a three-dimensional space by optimaztion combining Q-learning and Monte Carlo algorithms[J]. International Journal of Hybrid Information Technology, 2015, 8(11): 297–306. doi: 10.14257/ijhit.2015.8.11.25
[51]	JI Z J, WANG Z D, LIN H, et al. Interconnection topologies for multi-agent coordination under leader-follower framework[J]. Automatica, 2009, 45(12): 2857–2863. doi: 10.1016/j.automatica.2009.09.002
[52]	XIAO H Z, CHEN C L P. Leader-follower consensus multi-robot formation control using neurodynamic-optimization-based nonlinear model predictive control[J]. IEEE Access, 2019, 7: 43581–43590. doi: 10.1109/ACCESS.2019.2907960
[53]	LAWTON J R T, BEARD R W, YOUNG B J. A decentralized approach to formation maneuvers[J]. IEEE Transactions on Robotics & Automation, 2004, 19(6): 933–941.
[54]	JADBABAIE A, LIN J, MORSE A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules[J]. IEEE Transactions on Automatic Control, 2003, 48(6): 988–1001. doi: 10.1109/TAC.2003.812781
[55]	刘鸿福, 苏炯铭, 付雅晶. 无人系统集群及其对抗技术研究综述[J]. 飞航导弹, 2018(11): 35–40, 91. LIU H F, SU J M, FU Y J. A summary of the research on unmanned system clusters and their adversity techniques[J]. Aerodynamic Missile Journal, 2018(11): 35–40, 91 (in Chinese).
[56]	安梅岩, 王兆魁, 张育林. 人工智能集群控制演示验证系统[J]. 机器人, 2016, 38(3): 265–275. AN M Y, WANG Z K, ZHANG Y L. Demonstration and verification system for artificial intelligent swarm control[J]. Robot, 2016, 38(3): 265–275 (in Chinese).
[57]	DO K D, PAN J. Nonlinear formation control of unicycle-type mobile robots[J]. Robotics and Autonomous Systems, 2007, 55(3): 191–204. doi: 10.1016/j.robot.2006.09.001
[58]	RAMAZANI S, SELMIC R, DE QUEIROZ M. Rigidity-based multiagent layered formation control[J]. IEEE Transactions on Cybernetics, 2017, 47(8): 1902–1913. doi: 10.1109/TCYB.2016.2568164
[59]	ZHOU D J, WANG Z J, SCHWAGER M. Agile coordination and assistive collision avoidance for quadrotor swarms using virtual structures[J]. IEEE Transactions on Robotics, 2018, 34(4): 916–923. doi: 10.1109/TRO.2018.2857477
[60]	相晓嘉, 闫超, 王菖, 等. 基于深度强化学习的固定翼无人机编队协调控制方法[J/OL]. 航空学报, 2019: 40(X): 1–14. http://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2020.24009. XIANG X J, YAN C, WANG C, et al. Towards coordination control for fixed-wing UAV formation through deep reinforcement learning[J/OL]. Acta Aeronautica et Astronautica Sinica (in Chinese). http://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2020.24009.
[61]	LIU H, MENG Q Y, PENG F C, et al. Heterogeneous formation control of multiple UAVs with limited-input leader via reinforcement learning[J]. Neurocomputing, 2020, 412: 63–71. doi: 10.1016/j.neucom.2020.06.040
[62]	LA H M, SHENG W H. Distributed sensor fusion for scalar field mapping using mobile sensor networks[J]. IEEE Transactions on Cybernetics, 2013, 43(2): 766–778. doi: 10.1109/TSMCB.2012.2215919
[63]	王飞跃, 刘德荣, 熊刚, 等. 复杂系统的平行控制理论及应用[J]. 复杂系统与复杂性科学, 2012, 9(3): 1–12. doi: 10.3969/j.issn.1672-3813.2012.03.001 WANG F Y, LIU D R, XIONG G, et al. Parallel control theory of complex systems and applications[J]. Complex Systems and Complexity Science, 2012, 9(3): 1–12 (in Chinese). doi: 10.3969/j.issn.1672-3813.2012.03.001
[64]	LI L, WANG X, WANG K F, et al. Parallel testing of vehicle intelligence via virtual-real interaction[J]. Science Robotics, 2019, 4(28): eaaw4106. doi: 10.1126/scirobotics.aaw4106
[65]	朱冰, 张培兴, 赵健, 等. 基于场景的自动驾驶汽车虚拟测试研究进展[J]. 中国公路学报, 2019, 32(6): 1–19. ZHU B, ZHANG P X, ZHAO J, et al. Advances in virtual testing of self-driving cars based on scenarios[J]. China Journal of Highway Science, 2019, 32(6): 1–19 (in Chinese).
[66]	KANG Y, YIN H, BERGER C. Test your self-driving algorithm: an overview of publicly available driving datasets and virtual testing environments[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 171–185. doi: 10.1109/TIV.2018.2886678
[67]	范云生, 苏辉, 王国峰. 无人水面艇自主航行能力测试技术与应用[J]. 大连海事大学学报, 2020, 46(3): 38–49. FAN Y S, SU H, WANG G F. Testing technology and its application of autonomous navigation ability for unmanned surface vehicle[J]. Journal of Dalian Maritime University, 2020, 46(3): 38–49 (in Chinese).