Maritime Manned/unmanned Collaborative Systems and Key Technologies: A Survey

doi:10.12382/bgxb.2024.0327

Abstract

Abstract:

Maritime manned/unmanned collaborative systems play a crucial role in enhancing naval operational effectiveness and represent a significant direction in the development of modern naval equipment. This paper focuses on the development of maritime manned/unmanned collaborative systems, reviews the current developing status and relevant foreign projects, clarifies the main characteristics of the systems, condenses and analyzes the scientific issues and key technologies involved, and ultimately provides a comprehensive survey on the development of maritime manned/unmanned collaborative systems. In the future, intelligence, modularity and sparsity will become the important directions for the development of maritime manned/unmanned collaborative systems. However, due to the contradictions between system autonomy and controllability, the development of intelligence and maritime constraints, and the features of manned/unmanned collaboration and existing collaborative frameworks, the system will also face challenges in scale, diversity, security, and intelligence-related aspects.

Key words: maritime manned/unmanned collaborative system, unmanned system, unmanned swarm, swarm intelligence, human-machine cooperation

CLC Number:

WANG Tonghao, PENG Xingguang, HU Hao, XU Demin. Maritime Manned/unmanned Collaborative Systems and Key Technologies: A Survey[J]. Acta Armamentarii, 2024, 45(10): 3317-3340.

Figures/Tables 10

Fig.1 Two chains and two loops

Table 1 Level of Interoperability of manned/unmanned aerial systems issued by NATO in 2015

等级	互操作能力
1	可接收传感器关键数据
2	可接收传感器全部数据
3	可对无人机载荷进行监控和控制
4	可对无人机进行监控和控制(发射与回收除外)
5	可对无人机的发射与回收进行监控和控制

Fig.2 Systematic manned/unmanned collaborative system research plan of the U.S.

Fig.3 Components of the future maritime manned/unmanned collaborative system

Fig.4 Logical architecture for the key technologies

Fig.5 Design framework of the manned/unmanned collaborative task system

Fig.6 Framework of cognition and decision-making ability analysis and human-computer interaction technology

Fig.7 Framework of human-in-the-loop intelligent supporting decision-making technology

Fig.8 Framework of unmanned sensing and decision-making

Fig.9 Framework of system security assurance technology

References 145

[1]	王童豪, 彭星光, 潘光, 等. 无人水下航行器的发展现状与关键技术[J]. 宇航总体技术, 2017, 1(4): 52-64.
	WANG T H, PENG X G, PAN G, et al. Development and key technologies of unmanned underwater vehicles[J]. Astronautical Systems Engineering Technology, 2017, 1(4): 52-64. (in Chinese)
[2]	郑少秋, 吴浩, 梁汝鹏, 等. 智能化作战及其智能指控技术需求[J]. 火力与指挥控制, 2022, 47(2): 1-6, 13.
	ZHENG S Q, WU H, LIANG R P, et al. Intelligent operations and its intelligent command and control technology requirements[J]. Fire Control & Command Control, 2022, 47(2): 1-6, 13. (in Chinese)
[3]	张杰勇, 钟赟, 孙鹏, 等. 有人/无人机协同作战指挥控制系统技术[J]. 指挥与控制学报, 2021, 7(2): 203-214.
	ZHANG J Y, ZHONG Y, SUN P, et al. Command and control system and technology for manned/unmanned aerial vehicle cooperative operation[J]. Journal of Command and Control, 2021, 7(2): 203-214. (in Chinese)
[4]	牛轶峰, 沈林成, 李杰, 等. 无人-有人机协同控制关键问题[J]. 中国科学: 信息科学, 2019, 49(5): 538-554.
	NIU Y F, SHEN L C, LI J, et al. Key scientific problems in cooperation control of unmanned-manned aircraft systems[J]. Scienta Sinica Informationis, 2019, 49(5): 538-554. (in Chinese)
[5]	刘松, 徐洋洋. 国外海上有人-无人协同作战发展研究[J]. 电子技术, 2021, 50(7): 162-163.
	LIU S, XU Y Y. Study on the development of manned unmanned cooperative operations at sea abroad[J]. Electronic Technology, 2021, 50(7): 162-163. (in Chinese)
[6]	蔡亚梅, 宁勇, 郭涛. 美军有人-无人协同作战发展与趋势分析[J]. 航天电子对抗, 2021, 37(1): 12-18.
	CAI Y M, NING Y, GUO T. The development and trend of manned-unmanned collaborative operations of US army[J]. Aerospace Electronic Warfare, 2021, 37(1): 12-18. (in Chinese)
[7]	吴超, 杜辉, 何青海. 水下“有人/无人”作战平台协同运用方式探讨[J]. 舰船科学技术, 2020, 42(9): 153-156.
	WU C, DU H, HE Q H. Discussion on the cooperative use mode of underwater manned/unmanned combat platform[J]. Ship Science and Technology, 2020, 42(9): 153-156. (in Chinese)
[8]	陈杰, 辛斌. 有人/无人系统自主协同的关键科学问题[J]. 中国科学: 信息科学, 2018, 48(9): 1270-1274.
	CHEN J, XIN B. Key scientific problems in the autonomous cooperation of manned-unmanned systems[J]. SCIENTIA SINICA Informationis, 2018, 48(9): 1270-1274. (in Chinese)
[9]	WINNEFIELD J A, KENDALL F. Unmanned systems integrated roadmap FY2011—2036[R]. Washington, DC, US: U.S. Department of Defense, 2012: 1-108.
[10]	WINNEFIELD J A, KENDALL F. Unmanned systems integrated roadmap FY2013—2038[R]. Washington, DC, US: U.S. Department of Defense, 2014: 1-168.
[11]	FAHEY K M, MILLER M J. Unmanned systems integrated roadmap FY2017—2042[R]. Washington, DC, US: U.S. Department of Defense, 2018: 1-58.
[12]	BERGER D H. Commandants’ planning guidance[R]. Washington, DC, US: U.S. Marine Corps, 2019: 1-26.
[13]	NPS. The Disruptor[R]. Washington, DC, US: U.S. Department of the Navy, 2021: 1-16.
[14]	KOLLING A, WALKER P, CHAKRABORTY N, et al. Human interaction with robot swarms: a survey[J]. IEEE Transactions on Human-Machine Systems, IEEE, 2016, 46(1): 9-26.
[15]	DAS A, KOL P, LUNDBERG C, et al. A rapid situational awareness development framework for heterogeneous manned-unmanned teams[C]// Proceedings of IEEE National Aerospace Electronics Conference. Dayton, OH, US: IEEE, 2018: 417-424.
[16]	李兵, 廖志远, 滕传福, 等. 小型有人/无人协同特混编队对舰攻击策略研究[J]. 无人系统技术, 2020, 3(4): 58-65.
	LI B, LIAO Z Y, TENG C F, et al. Research on the attack strategy of small manned/unmanned coordinated special mixed formations on ships[J]. Unmanned Systems Technology, 2020, 3(4): 58-65. (in Chinese)
[17]	樊锐, 张鑫龙, 马磊, 等. 有人/无人机协同作战研究[J]. 中国电子科学研究院学报, 2020, 15(3): 230-236.
	FAN R, ZHANG X L, MA L, et al. Research on manned/unmanned aerial vehicle teaming[J]. Journal of China Academy of Electronics and Information Technology, 2020, 15(3): 230-236. (in Chinese)
[18]	初磊, 肖汉华, 王珊. 水下无人作战平台对潜艇作战影响研究[J]. 飞航导弹, 2013(1): 44-47.
	CHU L, XIAO H H, WANG S. Research on the impact of underwater unmanned warfare platforms on submarine operations[J]. Aerospace Technology, 2013(1): 44-47. (in Chinese)
[19]	霍聪, 高霄鹏, 柯永胜. 海上无人装备指挥人才能力需求分析[J]. 海军工程大学学报(综合版), 2021, 18(3): 54-58.
	HUO C, GAO X P, KE Y S. Analysis of capability of commanders for naval unmanned maritime equipment[J]. Journal of Naval University of Engineering (Comprehensive Edition), 2021, 18(3): 54-58. (in Chinese)
[20]	赵后雨. 潜水环境暴露对职业潜水员基本认知能力的影响[D]. 上海: 海军军医大学, 2021.
	ZHAO H Y. Effect of diving environment exposure on professional divers’ primary cognitive functions[D]. Shanghai: Naval Medical University, 2021. (in Chinese)
[21]	余苒, 徐鹏. 构建新时代海军舰艇部队心理保障层次模型[J]. 政工学刊, 2021, 486(5): 74-76.
	YU R, XU P. Constructing a hierarchical model of psychological support for naval vessels in the new era[J]. Political Work Journal, 2021, 486(5): 74-76. (in Chinese)
[22]	史建平, 赵彬, 蔡占魁, 等. 某水面舰艇官兵远洋训练一月时睡眠质量状况及影响因素分析[J]. 解放军预防医学杂志, 2020, 38(7): 6-8.
	SHI J P, ZHAO B, CAI Z K, et al. Analysis of sleep quality and influencing factors of officers and soldiers on a certain surface vessel during one month of ocean training[J]. Journal of Preventive Medicine of Chinese People’s Liberation Army, 2020, 38(7): 6-8. (in Chinese)
[23]	王春花. 舰艇、潜艇、守岛官兵心理健康对比分析[J]. 华南国防医学杂志, 2016, 30(4): 263-265.
	WANG C H. Mental health of different types of navy officers and soldiers[J]. Military Medicine of Joint Logistics, 2016, 30(4): 263-265. (in Chinese)
[24]	马强, 王静, 陈学伟, 等. 潜艇环境对艇员作业能力影响调查分析[J]. 军事医学, 2020, 44(6): 406-409.
	MA Q, WANG J, CHEN X W, et al. Influence of submarine environment on submariners’ operational performance[J]. Military Medical Sciences, 2020, 44(6): 406-409. (in Chinese)
[25]	魏存, 王云霞, 沈兴华. 潜艇艇员与潜艇岸勤人员领悟力、记忆力和作业能力对比研究[J]. 第二军医大学学报, 2019, 40(9): 1020-1023.
	WEI C, WANG Y X, SHEN X H. Comprehension, memory and job performance between submariners and off-shore staffs: a comparative study[J]. Academic Journal of Naval Medical University, 2019, 40(9): 1020-1023. (in Chinese)
[26]	彭丽, 徐津, 叶远鹏, 等. 潜艇环境对艇员认知功能和作业能力的影响[J]. 心理科学, 2017, 40(4): 934-940.
	PENG L, XU J, YE Y P, et al. The effect of submarine environment on crews’ cognitive function and performance ability[J]. Journal of Psychological Science, 2017, 40(4): 934-940. (in Chinese)
[27]	BLASCH E. Level 5 (user refinement) issues supporting information fusion management[C]// Proceedings of the 2006 9th International Conference on Information Fusion. Florence, Italy: IEEE, 2006, 5: 1-8.
[28]	PREECE A, BRAINES D, CERUTTI F, et al. Explainable AI for intelligence augmentation in multi-domain operations: arXiv:1910.07563[R]. Ithaca, NY, US: Cornell University, 2019(2019-10-16). https://arxiv.org/abs/1910.07563.
[29]	PREECE A, CERUTTI F, BRAINES D, et al. Cognitive computing for coalition situational understanding[C]// Proceedings of 2017 IEEE Smart World Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation. San Francisco, CA, US: IEEE, 2018: 1-6.
[30]	DUFOURD D, DALGALARRONDO A. Integrating human/robot interaction into robot control architectures for defense applications[C]// Proceedings of the 1st National Workshop on Control Architectures of Robots. Montpellier, France: Inria, 2006, 33: 1-19.
[31]	KOLLING A, SYCARA K, NUNNALLY S, et al. Human swarm interaction: an experimental study of two types of interaction with foraging swarms[J]. Journal of Human-Robot Interaction, 2013, 2(2): 104-129.
[32]	MCLURKIN J, SMITH J, FRANKEL J, et al. Speaking swarmish: human-robot interface design for large Swarms of autonomous mobile robots[C]// Proceedings of 2006 AAAI Spring Symposium. Stanford, CA, US: AAAI, 2006: 72-75.
[33]	SHANG C S, FANG H, CAI T, et al. Mixed initiative controller for simultaneous intervention, a model predictive control formulation[C]// Proceedings of IEEE International Conference on Intelligent Robots and Systems. Daejeon, Korea (South): IEEE, 2016: 2805-2810.
[34]	GRGIĆ-HLAČA N, ZAFAR M B, GUMMADI K P, et al. On fairness, diversity and randomness in algorithmic decision making: arXiv:1706.10208[J]. Ithaca, NY, US:Cornell University, 2017(2017-06-30). https://arxiv.org/abs/1706.10208.
[35]	MOSHAIOV A, AVIGAD G. Concept-based IEC for multi-objective search with robustness to human preference uncertainty[C]// Proceedings of 2006 IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada: IEEE, 2006: 1893-1900.
[36]	NALEPKA P, GREGORY-DUNSMORE J P, SIMPSON J, et al. Interaction flexibility in artificial agents teaming with humans[C]// Proceedings of the Annual Meeting of the Cognitive Science Society. Virtual: Cognitive Science Society, 2021: 112-118.
[37]	CARROLL M, SHAH R, HO M K, et al. On the utility of learning about humans for human-ai coordination[C]// Proceedings of the 33rd Conference on Neural Information Processing Systems. Vancouver, BC, Canada: ACM, 2019, 33: 1-12.
[38]	杨震. 面向有人-无人平台协同决策的策略智能推荐技术研究[D]. 长沙: 国防科技大学, 2018.
	YANG Z. Intelligent strategy recommendation technology research for manned-unmanned platform cooperative decision-making[D]. Changsha: National University of Defense Technology, 2018. (in Chinese)
[39]	吴慧垚, 徐杰, 葛贤亮. 基于认知架构的无人机操作员意图预测技术研究[C]// 第七届中国指挥控制大会论文集. 北京: 中国指挥与控制学会, 2019: 361-365.
	WU H Y, XU J, GE X L. Research on UAS operator’s intention prediction based on cognitive model[C]// Proceedings of the 7th China Command and Control Conference. Beijing: Chinese Institute of Command and control, 2019: 361-365. (in Chinese)
[40]	陈晓婧, 朱德政. 基于人工智能的战场态势感知和作战辅助决策[C]// 第九届中国指挥控制大会论文集. 北京: 中国指挥与控制学会, 2021: 578-582.
	CHEN X J, ZHU D Z. Battlefield situation sensing and operational auxiliary decision-making based on artificial intelligence[C]// Proceedings of the 9th China Command and Control Conference. Beijing: Chinese Institute of Command and Control, 2021: 578-582. (in Chinese)
[41]	WRIGHT J L, CHEN J Y C, LAKHMANI S G. Agent transparency and reliability in human-robot interaction: the influence on user confidence and perceived reliability[J]. IEEE Transactions on Human-Machine Systems, IEEE, 2020, 50(3): 254-263.
[42]	STOWERS K, KASDAGLIS N, RUPP M A, et al. The IMPACT of agent transparency on human performance[J]. IEEE Transactions on Human-Machine Systems, IEEE, 2020, 50(3): 245-253.
[43]	DAFOE A, BACHRACH Y, HADFIELD G, et al. Cooperative AI: machines must learn to find common ground[J]. Nature, 2021, 593(7857): 33-36.
[44]	VERED M, HOWE P, MILLER T, et al. Demand-Driven Transparency for Monitoring Intelligent Agents[J]. IEEE Transactions on Human-Machine Systems, 2020, 50(3): 264-275.
[45]	ROTH G, SCHULTE A, SCHMITT F, et al. Transparency for a workload-adaptive cognitive agent in a manned-unmanned teaming application[J]. IEEE Transactions on Human-Machine Systems, IEEE, 2020, 50(3): 225-233.
[46]	ABEYWICKRAMA D B, RAMCHURN S D. Engineering responsible and explainable models in human-agent collectives[J]. Applied Artificial Intelligence, 2024, 38(1): 2282834.
[47]	GELERNTER D. Mirror worlds:or the day software puts the universe in a shoebox... how it will happen and what it will mean[M]. Oxford, UK: Oxford University Press, 1993.
[48]	WANG T, LI J K, DENG Y J, et al. Digital twin for human-machine interaction with convolutional neural network[J]. International Journal of Computer Integrated Manufacturing, Taylor & Francis, 2021, 34(7/8): 888-897.
[49]	WANG T H, PENG X G, WANG T, et al. Automated design of action advising trigger conditions for multiagent reinforcement learning: a genetic programming-based approach[J]. Swarm and Evolutionary Computation, 2024, 85: 101475.
[50]	WANG T, PENG X G, WU Y P, et al. A GP based two-layer framework for data-driven modeling of swarm self-organizing rules[C]// Proceedings of 2019 IEEE Congress on Evolutionary Computation. Wellington, New Zealand: IEEE, 2019: 174-181.
[51]	CLABAUGH C, MATARIC M. Robots for the people, by the people: personalizing human-machine interaction[J]. Science Robotics, 2018, 3(21): 2-4.
[52]	CRANDALL J W, OUDAH M, TENNOM, et al. Cooperating with machines[J]. Nature Communications, 2018, 9(1): 233. doi: 10.1038/s41467-017-02597-8 pmid: 29339817
[53]	NAM C, WALKER P, LI H, et al. Models of trust in human control of swarms with varied levels of autonomy[J]. IEEE Transactions on Human-Machine Systems, IEEE, 2020, 50(3): 194-204.
[54]	THOROUGHMAN K A, SHADMEHR R. Learning of action through adaptive combination of motor primitives[J]. Nature, 2000, 407(6805): 742-747.
[55]	KONG Z, METTLER B. Modeling human guidance behavior based on patterns in agent-environment interactions[J]. IEEE Transactions on Human-Machine Systems, 2013, 43(4): 371-384.
[56]	NALEPKA P, LAMB M, KALLEN R W, et al. Human social motor solutions for human-machine interaction in dynamical task contexts[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(4): 1437-1446. doi: 10.1073/pnas.1813164116 pmid: 30617064
[57]	YUAN L Y, GAO X F, ZHENG Z L, et al. In situ bidirectional human-robot value alignment[J]. Science Robotics, 2022, 7(68): eabm4183.
[58]	ZHANG T, WANG H D, YUAN B, et al. Surrogate-assisted evolutionary q-learning for black-box dynamic time-linkage optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2023, 27(5): 1162-1176.
[59]	SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484-489.
[60]	YE D H, LIU Z, SUN M F, et al. Mastering complex control in MOBA games with deep reinforcement learning[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, NY, US: AAAI, 2020: 6672-6679.
[61]	WANG H C, TANG H Y, HAO J Y, et al. Large scale deep reinforcement learning in war-games[C]// Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. Seoul, Korea (South): IEEE, 2020: 1693-1699.
[62]	CHIANG J Y, CHEN Y C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE Transactions on Image Processing, IEEE, 2012, 21(4): 1756-1769.
[63]	GALDRAN A, PARDO D, PICÓN A, et al. Automatic Red-Channel underwater image restoration[J]. Journal of Visual Communication and Image Representation, 2015, 26(1): 132-145.
[64]	JIANG K, WANG Q, AN Z Y, et al. Mutual retinex: combining transformer and CNN for image enhancement[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(3): 224-2252.
[65]	ANWAR S, LI C Y. Diving deeper into underwater image enhancement: a survey[J]. Signal Processing: Image Communication, 2020, 89: 115978.
[66]	WANG N, CHEN T K, KONG X J, et al. Underwater attentional generative adversarial networks for image enhancement[J]. IEEE Transactions on Human-Machine Systems, 2023, 53(3): 490-500.
[67]	SUN S X, WANG H, ZHANG H, et al. Underwater Image enhancement with reinforcement learning[J]. IEEE Journal of Oceanic Engineering, 2024, 49(1): 249-261.
[68]	PENG L T, ZHU C L, BIAN L H. U-shape transformer for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2023, 32: 3066-3079. doi: 10.1109/TIP.2023.3276332 pmid: 37200123
[69]	WANG X M, JIAO J, YIN J W, et al. Underwater sonar image classification using adaptive weights convolutional neural network[J]. Applied Acoustics, 2019, 146: 145-154.
[70]	CHUNGATH T T, NAMBIAR A M, MITTAL A. Transfer learning and few-shot learning based deep neural network models for underwater sonar image classification with a few samples[J]. IEEE Journal of Oceanic Engineering, 2024, 49(1): 294-310.
[71]	MUÑOZ B, TRONI G. Learning the ego-motion of an underwater imaging sonar: a comparative experimental evaluation of novel CNN and RCNN approaches[J]. IEEE Robotics and Automation Letters, 2024, 9(3): 2072-2079.
[72]	WANG Z, XIANG X B, GUAN X W, et al. Deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle[J]. Ocean Engineering, 2024, 293: 116704.
[73]	李洪, 张大铭, 严晞隽, 等. 面向海洋探测的多源信息融合技术研究及展望[J]. 宇航总体技术, 2021, 5(4): 1-6.
	LI H, ZHANG D M, YAN X J, et al. Research and prospect of multi-source information fusion technology for ocean detection[J]. Astronautical Systems Engineering Technology, 2021, 5(4): 1-6. (in Chinese)
[74]	PU Y Y, ZHU C Y, YANG K X, et al. A novel AMSS-FFN for underwater multisource localization using artificial lateral line[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 2530214.
[75]	LI C Y, GUO S X. Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots[J]. Information Fusion, 2023, 95: 199-214.
[76]	CAZAU D, NGUYEN HONG DUC P, DRUON J N, et al. Multimodal deep learning for cetacean distribution modeling of fin whales (Balaenoptera physalus) in the western Mediterranean Sea[J]. Machine Learning, 2023, 112(6): 2003-2024.
[77]	张可为, 赵晓林, 李宗哲, 等. 多无人机侦察任务分配方法研究综述[J]. 电光与控制, 2021, 28(7): 68-72, 82.
	ZHANG K W, ZHAO X L, LI Z Z, et al. A review of multi-uav reconnaissance mission assignment methods[J]. Electronics Optics & Control, 2021, 28(7): 68-72, 82. (in Chinese)
[78]	QIAN T, LIU X F, FANG Y C. A cooperative ant colony system for multiobjective multirobot task allocation with precedence constraints[J/OL]. IEEE Transactions on Evolutionary Computation, 2024(2024-15). https://ieeexplore.ieee.org/document/10438023.
[79]	XU S F, BI W H, ZHANG A, et al. Optimization of flight test tasks allocation and sequencing using genetic algorithm[J]. Applied Soft Computing, 2022, 115: 108241.
[80]	LIU X F, FANG Y, ZHAN Z H, et al. Strength learning particle swarm optimization for multiobjective multirobot task scheduling[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(7): 4052-4063.
[81]	ZHAO B, ZHANG Y W, LIU D R. Adaptive dynamic programming-based cooperative motion/force control for modular reconfigurable manipulators: a joint task assignment approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(12): 10944-10954.
[82]	李鑫滨, 郭力争, 韩松. 一种分布式异构多AUV任务分配鲁棒拍卖算法[J]. 北京航空航天大学学报, 2020, 48(5): 736-746.
	LI X B, GUO L Z, HAN S. A robust auction algorithm for distributed heterogeneous multi-AUV task assignment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 48(5): 736-746. (in Chinese)
[83]	FERRI G, MUNAFO A, TESEI A, et al. A market-based task allocation framework for autonomous underwater surveillance networks[C]// Proceedings of OCEANS 2017. Aberdeen, UK: IEEE, 2017: 1-10.
[84]	MAHMOUD ZADEH S, POWERS D M W, SAMMUT K, et al. A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment[J]. Soft Computing, 2018, 22(5): 1687-1710.
[85]	HE H X, DUAN H B, YUAN W M, et al. A potential game approach to target assignment in heterogeneous manned/unmanned aerial team with incomplete information[J/OL]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024(2024-06-19). https://ieeexplore.ieee.org/abstract/document/10155322.
[86]	JI Y X, NI L T, ZHAO C, et al. TriPField: a 3D potential field model and its applications to local path planning of autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(3): 3541-3554.
[87]	岳伟, 席云, 关显赫. 基于多蚁群协同搜索算法的多 AUV 路径规划[J]. 水下无人系统学报, 2020, 28(5): 505-511.
	YUE W, XI Y, GUAN X H. Path planning of multi-auvs based on multi-ant colony cooperative search algorithm[J]. Journal of Unmanned Undersea Systems, 2020, 28(5): 505-511. (in Chinese)
[88]	XU L, CAO X B, DU W B, et al. Cooperative path planning optimization for multiple UAVs with communication constraints[J]. Knowledge-Based Systems, 2023, 260: 110164.
[89]	HU H, ZHANG Z, WANG T H, et al. Underwater glider 3D path planning with adaptive segments and optimal motion parameters based on improved JADE algorithm[J]. Ocean Engineering, 2024, 299: 117377.
[90]	CUI R X, LI Y, YAN W S. Mutual information-based multi-auv path planning for scalar field sampling using multidimensional RRT^*[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE, 2016, 46(7): 993-1004.
[91]	HE Z C, DONG L, SUN C Y, et al. Asynchronous multithreading reinforcement-learning-based path planning and tracking for unmanned underwater vehicle[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(5): 2757-2769.
[92]	ZHANG J, REN J, CUI Y N, et al. Multi-USV task planning method based on improved deep reinforcement learning[J]. IEEE Internet of Things Journal, 2024, 11(10): 18549-18567.
[93]	PANG W, ZHU D Q, LIU C X, et al. The multi-AUV time-varying formation reconfiguration control based on rigid-graph theory and affine transformation[J]. Ocean Engineering, 2023, 270: 113521.
[94]	YAN J, WANG H Y, YANG X, et al. Optimal rigid graph-based cooperative formation control of AUVs in anchor-free environments[J/OL]. IEEE Transactions on Intelligent Vehicles, 2023(2023-08-18). https://ieeexplore.ieee.org/document/10224348.
[95]	YANG Y H, MEI J, MA G F. Formation of time-varying multi-auvs under directed graphs with event-triggered control: an online model-free parameter learning[J/OL]. IEEE Transactions on Intelligent Vehicles, 2024(2024-03-18). https://ieeexplore.ieee.org/document/10468617.
[96]	LI H P, XIE P, YAN W S. Receding horizon formation tracking control of constrained underactuated autonomous underwater vehicles[J]. IEEE Transactions on Industrial Electronics, IEEE, 2017, 64(6): 5004-5013.
[97]	THUYEN N A, THANH P N N, ANH H P H. Adaptive finite-time leader-follower formation control for multiple AUVs regarding uncertain dynamics and disturbances[J]. Ocean Engineering, 2023, 269: 113503.
[98]	LIANG Q W, SUN T Y, WANG D D. Time-varying reliability indexes for multi-AUV cooperative system[J]. Journal of Systems Engineering and Electronics, 2017, 28(2): 401-406. doi: 10.21629/JSEE.2017.02.20
[99]	YUAN C Z, LICHT S, HE H B. Formation learning control of multiple autonomous underwater vehicles with heterogeneous nonlinear uncertain dynamics[J]. IEEE Transactions on Cybernetics, IEEE, 2018, 48(10): 2920-2934.
[100]	CERVANTES J, YU W, SALAZAR S, et al. Output based backstepping control for trajectory tracking of an Autonomous Underwater Vehicle[C]// Proceedings of 2016 American Control Conference. Boston, MA, US: IEEE, 2016: 6423-6428.
[101]	方一成. 多AUV路径规划与编队控制研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
	FANG Y C. Research on multiple auv path planning and formation control[D]. Harbin: Harbin Engineering University, 2018. (in Chinese)
[102]	WANG L L, ZHU D Q, PANG W, et al. A novel obstacle avoidance consensus control for multi-auv formation system[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(5): 1304-1318.
[103]	HADI B, KHOSRAVI A, SARHADI P. Adaptive formation motion planning and control of autonomous underwater vehicles using deep reinforcement learning[J]. IEEE Journal of Oceanic Engineering, 2024, 49(1): 311-328.
[104]	刘明雍, 雷小康, 杨盼盼, 等. 群集运动的理论建模与实证分析[J]. 科学通报, 2014, 59(25): 2464-2483.
	LIU M Y, LEI X K, YANG P P, et al. Progress of theoretical modelling and empirical studies on collective motion[J]. Chinese Science Bulletin, 2014, 59(25): 2464-2483. (in Chinese)
[105]	VÁSÁRHELYI G, VIRÁGH C, SOMORJAI G, et al. Optimized flocking of autonomous drones in confined environments[J]. Science Robotics, 2018, 3(20): eaat3536.
[106]	BALÁZS B, VÁSÁRHELYI G, VICSEK T. Adaptive leadership overcomes persistence-responsivity trade-off in flocking[J]. Journal of the Royal Society Interface, 2020, 17(167): 20190853.
[107]	ZHANG S, LEI X K, DUAN M Y, et al. A distributed outmost push approach for multirobot herding[J]. IEEE Transactions on Robotics, 2024, 40: 1706-1723.
[108]	HU J Q, WU H S, ZHAN R J, et al. Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1463-1476. doi: 10.23919/JSEE.2021.000124
[109]	ZOU B Y, PENG X G. A bilateral cooperative strategy for swarm escort under the attack of aggressive swarms[J]. Electronics, 2022, 11(22): 3643.
[110]	RUAN W, SUN Y, DENG Y, et al. Hawk-pigeon game tactics for unmanned aerial vehicle swarm target defense[J]. IEEE Transactions on Industrial Informatics, 2023, 19(12): 11619-11629.
[111]	ZHOU Y J, WANG T H, LEI X K, et al. Collective behavior of self-propelled particles with heterogeneity in both dynamics and delays[J]. Chaos, Solitons & Fractals, 2024, 180(62076203): 114596.
[112]	KHUONG A, GAUTRAIS J, PERNA A, et al. Stigmergic construction and topochemical information shape ant nest architecture[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(5): 1303-1308. doi: 10.1073/pnas.1509829113 pmid: 26787857
[113]	ZHOU Y J, WANG T H, PENG X G. Collective behavior of self-propelled particles with multiple delays in cross-domain scenario[J]. New Journal of Physics, 2023, 25(11): 113044.
[114]	田磊, 王晓东, 董希旺, 等. 基于时变编队控制的有人-无人集群协同飞行策略[J]. 指挥与控制学报, 2022, 8(1): 57-63.
	TIAN L, WANG X D, DONG X W, et al. Cooperative flight strategy for manned and unmanned swarm systems based on time-varying formation control[J]. Journal of Command and Control, 2022, 8(1): 57-63. (in Chinese)
[115]	陶伟. 基于马赛克战的水下有人-无人集群控制结构[J]. 指挥与控制学报, 2020, 6(3): 264-270.
	TAO W. Control structure of underwater manned and unmanned swarm based on mosaic warfare[J]. Journal of Command and Control, 2020, 6(3): 264-270. (in Chinese)
[116]	张建华, 黄海峰, 胡坤, 等. 密度跃层对潜艇操纵运动的影响及应对措施研究[J]. 兵器装备工程学报, 2021, 42(4): 118-122.
	ZHANG J H, HUANG H F, HU K, et al. Study on influence of pycnocline on submarine maneuverability and countermeasures[J]. Journal of Ordnance Equipment Engineering, 2021, 42(4): 118-122. (in Chinese)
[117]	刘健, 李冬冬, 冀大雄. AUV海洋温跃层检测方法综述[J]. 海洋技术学报, 2014, 33(5): 127-136.
	LIU J, LI D D, JI D X. The methods of detecting thermoclines in oceans by AUV: a review[J]. Journal of Ocean Technology, 2014, 33(5): 127-136. (in Chinese)
[118]	ZHANG Y W, BELLINGHAM J G, GODIN M A, et al. Using an autonomous underwater vehicle to track the thermocline based on peak-gradient detection[J]. IEEE Journal of Oceanic Engineering, IEEE, 2012, 37(3): 544-553.
[119]	高飞, 潘长明, 张韧, 等. 温跃层及其变化对被动声纳检测概率影响的研究[J]. 应用声学, 2014, 33(2): 138-144.
	GAO F, PAN C M, ZHANG R, et al. The effects of thermocline and its variability on the detection probability of passive sonar[J]. Journal of Applied Acoustics, 2014, 33(2): 138-144. (in Chinese)
[120]	祝捍皓, 肖瑞, 朱军, 等. 三维浅海环境下孤立子内波对低频声能流的传播影响[J]. 声学学报, 2021, 46(3): 365-374.
	ZHU H H, XIAO R, ZHU J, et al. Influence of internal solitary waves on sound propagation in three-dimensional shallow sea[J]. Acta Acustica, 2021, 46(3): 365-374. (in Chinese)
[121]	马雨薇, 韩东, 魏尚飞, 等. 深海高频内波对潜艇水声通信和探测的影响分析[J]. 电声技术, 2021, 45(3): 1-9, 12.
	MA Y W, HAN D, WEI S F, et al. The influence of deep ocean high frequency internal waves on submarine acoustic communication and detection[J]. Audio Engineering, 2021, 45(3): 1-9, 12. (in Chinese)
[122]	胡涛, 王臻, 郭圣明, 等. 利用声场简正波幅度起伏反演内波传播速度[J]. 哈尔滨工程大学学报, 2020, 41(10): 1518-1523.
	HU T, WANG Z, GUO S M, et al. Inversion of the internal wave velocity using the normal-mode amplitude fluctuation of anderwater sound field[J]. Journal of Harbin Engineering University, 2020, 41(10): 1518-1523. (in Chinese)
[123]	潘长明, 高飞, 王璐华, 等. 浅海温跃层对水声传播损失场的影响[J]. 哈尔滨工程大学学报, 2014, 35(4): 401-407.
	PAN C M, GAO F, WANG L H, et al. The effects of shallow water thermocline on water acoustic transmission loss field[J]. Journal of Harbin Engineering University, 2014, 35(4): 401-407. (in Chinese)
[124]	RAANAN B Y, BELLINGHAM J, ZHANG Y, et al. Detection of unanticipated faults for autonomous underwater vehicles using online topic models[J]. Journal of Field Robotics, 2018, 35(5): 705-716.
[125]	WANG X H. Active fault tolerant control for unmanned underwater vehicle with sensor faults[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(12): 9485-9495.
[126]	FAN C F, CHAN C C, YU H Y, et al. A simulation platform for human-machine interaction safety analysis of cyber-physical systems[J]. International Journal of Industrial Ergonomics, 2018, 68: 89-100.
[127]	刘俊凯, 董阳泽, 张刚强. 隐蔽通信中基于水声信道的密钥生成技术[J]. 应用声学, 2019, 38(4): 681-687.
	LIU J K, DONG Y Z, ZHANG G Q. Key generation technology based on underwater acoustic channel estimation in covert communication[J]. Journal of Applied Acoustics, 2019, 38(4): 681-687. (in Chinese)
[128]	张俊清, 张刚强, 刘俊凯. 基于度分布的水声通信网络干扰节点部署方法[J]. 声学与电子工程, 2021, 141(1): 31-34.
	ZHANG J Q, ZHANG G Q, LIU J K. Deployment method of interference nodes in underwater acoustic communication networks based on degree distribution[J]. Acoustics and Electronics Engineering, 2021, 141(1): 31-34. (in Chinese)
[129]	徐明, 范以萌, 蒋昌俊. 基于时变水声信道的物理层密钥生成方案[J]. 计算机研究与发展, 2019, 56(12): 2660-2670.
	XU M, FAN Y M, JIANG C J. Time-varying underwater acoustic channel based physical layer secret key generation scheme[J]. Journal of Computer Research and Development, 2019, 56(12): 2660-2670. (in Chinese)
[130]	LUO Y, PU L N, PENG Z, et al. RSS-based secret key generation in underwater acoustic networks: advantages, challenges, and performance improvements[J]. IEEE Communications Magazine, 2016, 54(2): 32-38.
[131]	HUANG Y, ZHOU S L, SHI Z J, et al. Channel frequency response-based secret key generation in underwater acoustic systems[J]. IEEE Transactions on Wireless Communications, 2016, 15(9): 5875-5888.
[132]	XU M, FAN Y M, LIU L. Multi-party secret key generation over underwater acoustic channels[J]. IEEE Wireless Communications Letters, 2020, 9(7): 1075-1079.
[133]	吴晖, 翟月琛, 张东钥. 水下声呐通信安全研究[J]. 信息技术与网络安全, 2021, 40(8): 24-29.
	WU H, ZHAI Y C, ZHANG D Y. Research on security of underwater sonar communication[J]. Cyber Security and Data Governance, 2021, 40(8): 24-29. (in Chinese)
[134]	彭春燕, 杜秀娟. 水声传感器网络基于深度信息的安全路由协议[J]. 计算机工程与应用, 2020, 56(2): 76-81. doi: 10.3778/j.issn.1002-8331.1903-0359
	PENG C Y, DU X J. Secure routing protocol based on depth in underwater acoustic sensor networks[J]. Computer Engineering and Applications, 2020, 56(2): 76-81. (in Chinese) doi: 10.3778/j.issn.1002-8331.1903-0359
[135]	LI X B, ZHOU Y, YAN L, et al. Optimal node selection for hybrid attack in underwater acoustic sensor networks: a virtual expert-guided bandit algorithm[J]. IEEE Sensors Journal, 2020, 20(3): 1679-1687.
[136]	颛孙少帅, 杨俊安, 刘辉, 等. 基于强化学习的无线自组网络多节点干扰策略[J]. 控制与决策, 2018, 33(7): 1199-1206.
	ZHUANGSUN S S, YANG J A, LIU H, et al. Multi-nodes jamming strategy in wireless Ad hoc network based on reinforcement learning[J]. Control and Decision, 2018, 33(7): 1199-1206. (in Chinese)
[137]	AMURU S, MICHAEL BUEHRER R, van der SCHAAR M. Blind network interdiction strategies-a learning approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2015, 1(4): 435-449.
[138]	黄亮平. 水声通信网的认证和入侵检测技术研究[D]. 南京: 东南大学, 2019.
	HUANG L P. The study of authentication and intrusion detection technology of underwater acoustic communication network[D]. Nanjing: Southeast University, 2019. (in Chinese)
[139]	YI L, LI M Y, LIU S X, et al. Detection of dawn sea fog/low stratus using geostationary satellite imagery[J]. Remote Sensing of Environment, 2023, 294: 113622.
[140]	MACFARLANE A R, SCHNEEBELI M, DADIC R, et al. A database of snow on sea ice in the central arctic collected during the MOSAiC expedition[J]. Scientific Data, 2023, 10(1): 398. doi: 10.1038/s41597-023-02273-1 pmid: 37349340
[141]	SUN S, LYU H G, GAO Z J, et al. Grid map assisted radar target tracking in a detection occluded maritime environment[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 8502711.
[142]	LI C, WANG H D, YAO W, et al. Adversarial attacks in computer vision: a survey[J]. Journal of Membrane Computing, 2024, 6(2): 130-147.
[143]	ZHU X P, HU Z H, HUANG S Y, et al. Infrared invisible clothing: hiding from infrared detectors at multiple angles in real world[C]// Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, LA, US: IEEE, 2022: 13307-13316.
[144]	LI C, YAO W, WANG H D, et al. Bayesian evolutionary optimization for crafting high-quality adversarial examples with limited query budget[J]. Applied Soft Computing, 2023, 142: 110370.
[145]	BYMAN D L, GAO C Y, MESEROLE C, et al. Deepfakes and international conflict[M]. Washington, DC, US: Brookings Institution, 2023.