海洋无人航行器异构编队控制技术：现状、挑战与展望

doi:10.12305/j.issn.1001-506X.2026.01.28

Abstract

Abstract:

Heterogeneous formation of unmanned marine vehicles （UMVs） is a key cooperative operation mode in the future sea battlefield. In this paper, the concept and classification of heterogeneous formation of UMV and its current technological development are summarized, the research progress and main challenges of key technologies of UMV heterogeneous formation control from the aspects of control architectures, control theories, and communications are analyzed, and the future development direction is discussed from the aspects of intelligent control based on data-driven, communication constraints oriented combination of communication and control, building interoperability architecture, and intelligent fault-tolerant control.

Key words: unmanned marine vehicle, heterogeneous formation, control strategy, communication constraints

CLC Number:

Wenkui LI, Yawen ZHANG, Wunong XU, Yuhao YANG, Peisen JIN, Chenyang SHAN. Heterogeneous formation control technology for unmanned marine vehicles: current status, challenges and perspectives[J]. Systems Engineering and Electronics, 2026, 48(1): 312-330.

Figures/Tables 12

Fig.1

Fig.2

Table 1

Table 2

Table 3

Fig.3

Table 4

Fig.4

Fig.5

Fig.6

Table 5

Table 6

References 87

87	ZHENG H B. Research and implementation of aquatic-aerial trans-domain cooperative formation for unmanned surface vessels and unmanned aerial vehicles[D]. Wuhan: Huazhong University of Science and Technology, 2022.
88	王广旭, 蔡伟, 李永琪, 等. 含不确定动态观测的异构无人舰艇系统固定时间编队控制[J]. 电光与控制, 2024, 32 (5): 103- 110.
	WANG G X, CAI W, LI Y Q, et al. Fixed-time formation control for heterogeneous unmanned surface vehicles with uncertain dynamic observations[J]. Electronics Optics and Control, 2024, 32 (5): 103- 110.
89	白嘉琪, 王彦恺, 邢昊. 无人艇与四旋翼无人机固定时间异构编队控制[J]. 系统工程与电子技术, 2023, 45 (4): 1152- 1163. doi: 10.12305/j.issn.1001-506X.2023.04.24
	BAI J Q, WANG Y K, XING H. Fixed time heterogeneous formation control of unmannedboats and quadrotor unmanned aerial vehicle[J]. Systems Engineering and Electronics, 2023, 45 (4): 1152- 1163. doi: 10.12305/j.issn.1001-506X.2023.04.24
90	DONG X W, WANG Q, YU J L, et al. Neuroadaptive output formation tracking for heterogeneous nonlinear multiagent systems with multiple nonidentical leaders[J]. IEEE Trans. on Neural Networks and Learning Systems, 2022, 35 (3): 3702- 3712.
91	李娟, 袁锐锟, 张宏瀚. 基于领航跟随法的多AUV编队控制算法研究[J]. 仪器仪表学报, 2022, 40 (6): 237- 246. doi: 10.19650/j.cnki.cjsi.J1904889
	LI J, YUAN R K, ZHANG H H. Research on multiple AUVs formation control algorithm based on leader follower method[J]. Chinese Journal of Seientifie Instrument, 2022, 40 (6): 237- 246. doi: 10.19650/j.cnki.cjsi.J1904889
92	OGREN P, FIORELLI E, LEONARD N E. Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment[J]. IEEE Trans. on Automatic Control, 2004, 49 (8): 1292- 1302. doi: 10.1109/TAC.2004.832203
93	YAO X Y, DING H F, GE M F. Formation-containment control for multi-robot systems with two-layer leaders via hierarchical controller–estimator algorithms[J]. Journal of the Franklin Institute, 2018, 355 (12): 5272- 5290. doi: 10.1016/j.jfranklin.2018.05.010
94	PETERSON C K, BARTON J. Virtual structure formations of cooperating UAVs using wind-compensation command generation and generalized velocity obstacles[C]// Proc. of the IEEE Aerospace Conference, 2015.
95	郑海滨, 龙铭康, 苏厚胜, 等. 虚拟结构与人工势场相结合的机艇协同编队[C]// 第40届中国控制会议, 2021: 8897−8902.
	ZHENG H B, LONG M K, SU H S, et al. Synergy formation of UAV and USV combining virtual structure and artificial potential field[C]// Proc. of the 40th Chinese Control Conference, 2021: 8897−8902.
96	胡建章, 唐国元, 王建军, 等. 基于自适应反步滑模的水面无人艇集群控制[J]. 中国舰船研究, 2019, 14 (6): 1- 7. doi: 10.19693/j.issn.1673-3185.01521
	HU J G, TANG G Y, WANG J J, et al. Swarm control of USVs based on adaptive backstepping combined with sliding mode[J]. Chinese Journal of Ship Research, 2019, 14 (6): 1- 7. doi: 10.19693/j.issn.1673-3185.01521
97	王宁, 刘永金, 高颖. 未知扰动下的无人艇编队优化轨迹跟踪控制[J]. 中国舰船研究, 2024, 19 (1): 178- 190. doi: 10.19693/j.issn.1673-3185.03348
	WANG N, LIU Y J, GAO Y. Optimal trajectory tracking control of unmanned surface vehicle formation under unknown disturbances[J]. Chinese Journal of Ship Research, 2024, 19 (1): 178- 190. doi: 10.19693/j.issn.1673-3185.03348
98	LIU G, WEN N, LONG F, et al. A formation control and obstacle avoidance method for multiple unmanned surface vehicles[J]. Journal of Marine Science and Engineering, 2023, 11 (12): 2346. doi: 10.3390/jmse11122346
99	LAWTON J R T. A behavior-based approach to multiple spacecraft formation flying[M]. Provo, Utah: Brigham Young University, 2000.
100	CAO Z Q, TAN M, WANG S, et al. The optimization research of formation control for multiple mobile robots[C]// Proc. of the 4th World Congress on Intelligent Control and Automation, 2002: 1270−1274.
101	MONTEIRO S, BICHO E. A dynamical systems approach to behavior-based formation control[C]// Proc. of the IEEE International Conference on Robotics and Automation, 2002: 2606−2611.
102	JIA Q L, LI G W. Formation control and obstacle avoidance algorithm of multiple autonomous underwater vehicles (AUVs) based on potential function and behavior rules[C]// Proc. of the IEEE International Conference on Automation and Logistics, 2007: 569−573.
103	ANTONELLI G, ARRICHIELLO F, CHIAVERINI S. Experiments of formation control with multirobot systems using the null-space-based behavioral control[J]. IEEE Trans. on Control Systems Technology, 2009, 17 (5): 1173- 1182. doi: 10.1109/TCST.2008.2004447
104	ARRICHIELLO F, CHIAVERINI S, INDIVERI G, et al. The null-space-based behavioral control for mobile robots with velocity actuator saturations[J]. The International Journal of Robotics Research, 2010, 29 (10): 1317- 1337. doi: 10.1177/0278364909358788
1	PENG Z H, WANG J, WANG D, et al. An overview of recent advances in coordinated control of multiple autonomous surface vehicles[J]. IEEE Trans. on Industrial Informatics, 2020, 17 (2): 732- 745.
2	金克帆, 王鸿东, 易宏, 等. 海上无人装备关键技术与智能演进展望[J]. 中国舰船研究, 2018, 13 (6): 1- 8. doi: 10.19693/j.issn.1673-3185.01293
	JIN K F, WANG H D, YI H, et al. Key technologies and intelligence evolution of maritime UV[J]. Chinese Journal of Ship Research, 2018, 13 (6): 1- 8. doi: 10.19693/j.issn.1673-3185.01293
3	徐博, 王朝阳. 基于无人艇跨域异构编队协同导航研究进展与未来趋势[J]. 中国舰船研究, 2022, 17 (4): 1- 11，56. doi: 10.19693/j.issn.1673-3185.02389
	XU B, WANG Z Y. Cooperative navigation of cross-domain heterogeneous unmanned ship formation: recent advances and future trends[J]. Chinese Journal of Ship Research, 2022, 17 (4): 1- 11，56. doi: 10.19693/j.issn.1673-3185.02389
4	车高峰. 自主式水下航行器路径规划及轨迹跟踪控制的研究[D]. 厦门: 厦门大学, 2021.
	CHE G F. Researches on path planning and trajectory-tracking control for autonomous underwater vehicle[D]. Xiamen: Xiamen University, 2021.
5	胡满江, 王智炜, 杜长坤, 等. 基于动态事件触发策略的多AUV一致控制[J]. 湖南大学学报（自然科学版）, 2022, 49 (8): 1- 11. doi: 10.16339/j.cnki.hdxbzkb.2022177
	HU M J, WANG Z W, DU C K, et al. Consensus control of multi-AUVs based on a dynamic event-triggered strategy[J]. Journal of Hunan University（Natural Sciences）, 2022, 49 (8): 1- 11. doi: 10.16339/j.cnki.hdxbzkb.2022177
6	梁霄. 无人水下航行器集群控制[M]. 北京: 科学出版社, 2023.
	LIANG X. Cluster control of unmanned underwater vehicles[M]. Beijing: Press of Science, 2023.
7	OLFATI-SABER R. Flocking for multi-agent dynamic systems: algorithms and theory[J]. IEEE Trans. on Automatic Control, 2006, 51 (3): 401- 420. doi: 10.1109/TAC.2005.864190
105	TAN G G, ZHUANG J Y, ZOU J, et al. Coordination control for multiple unmanned surface vehicles using hybrid behavior-based method[J]. Ocean Engineering, 2021, 232, 109147. doi: 10.1016/j.oceaneng.2021.109147
106	FAN J J, LIAO Y L, LI Y, et al. Formation control of multiple unmanned surface vehicles using the adaptive null-space-based behavioral method[J]. IEEE Access, 2019, 7, 87647- 87657. doi: 10.1109/ACCESS.2019.2925466
107	PENG Z, WANG D, CHEN Z Y, et al. Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics[J]. IEEE Trans. on Control Systems Technology, 2012, 21 (2): 513- 520.
108	WU H, SONG S J, YOU K Y, et al. Depth control of model-free AUVs via reinforcement learning[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2018, 49 (12): 2499- 2510.
109	WANG H Q, SHI P, LI H Y, et al. Adaptive neural tracking control for a class of nonlinear systems with dynamic uncertainties[J]. IEEE Trans. on Cybernetics, 2016, 47 (10): 3075- 3087.
110	ZHANG X Y, HAN T, XIAO B, et al. Neural network-based predefined-time bipartite formation tracking control of uncertain heterogeneous Euler-Lagrange systems in task space[J]. ISA Transactions, 2024, 148, 358- 366. doi: 10.1016/j.isatra.2024.03.013
111	GUO X X, CUI R X, YAN W S. Pursuit-evasion games of marine surface vessels using neural network-based control[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2024, 55 (1): 18- 27.
112	YUAN C Z, LICHT S, HE H B. Formation learning control of multiple autonomous underwater vehicles with heterogeneous nonlinear uncertain dynamics[J]. IEEE Trans. on Cybernetics, 2017, 48 (10): 2920- 2934.
113	PENG Z H, WANG D, LI T S, et al. Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics[J]. Nonlinear Dynamics, 2013, 74 (1/2): 95- 106. doi: 10.1007/s11071-013-0951-3
114	LIU X M, GE S S, GOH C H. Neural-network-based switching formation tracking control of multiagents with uncertainties in constrained space[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2017, 49 (5): 1006- 1015.
115	CHEN L P, CUI R X, YANG C G, et al. Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: theory and experimental results[J]. IEEE Trans. on Industrial Electronics, 2019, 67 (5): 4024- 4035.
116	LIU H T, WENG P J, TIAN X H, et al. Distributed adaptive fixed-time formation control for UAV-USV heterogeneous multi-agent systems[J]. Ocean Engineering, 2023, 267, 113240. doi: 10.1016/j.oceaneng.2022.113240
8	陈盼, 胡剑光, 尹志伟. UUV 编队协同搜索静止目标的准最优方法[J]. 火力与指挥控制, 2013, 38 (4): 53- 56. doi: 10.3969/j.issn.1002-0640.2013.04.013
	CHEN P, HU J G, YIN Z W. Quasi-optimal method for multiple UUVs cooperate to search static target[J]. Fire Control and Command Control, 2013, 38 (4): 53- 56. doi: 10.3969/j.issn.1002-0640.2013.04.013
9	严浙平. 水下无人航行器控制技术[M]. 北京: 国防工业出版社, 2015.
	YAN Z P. Control technology for unmanned underwater vehicles[M]. Beijing: Press of National Defence Industry, 2015.
10	曾江峰, 谢杨柳, 金哲毅, 等. 水面无人艇集群编队控制技术综述[J]. 导航定位与授时, 2023, 10 (5): 7- 17. doi: 10.19306/j.cnki.2095-8110.2023.05.002
	ZENG J F, XIE Y L, JIN Z Y, et al. Review for swarm formation control technology of unmanned surface vehicles[J]. Navigation Positioning and Timing, 2023, 10 (5): 7- 17. doi: 10.19306/j.cnki.2095-8110.2023.05.002
11	邹佳运, 张文清, 曲泓玥, 等. 面向水下目标搜索的USV&UUV编队协同队形规划[J]. 无人系统技术, 2021, 4 (3): 40- 46.
	ZOU J Y, ZHANG W Q, QU H Y, et al. Cooperative formation planning of UUV&USV formations for underwater target search[J]. Unmanned Systems Technology, 2021, 4 (3): 40- 46.
12	徐小斌, 段海滨, 曾志刚, 等. 无人机/无人艇协同控制研究进展[J]. 航空兵器, 2020, 27 (6): 1- 6. doi: 10.12132/ISSN.1673-5048.2020.0227
	XU X B, DUAN H B, ZENG Z G, et al. Progresses in UAV/USV cooperative control[J]. Aero Weaponry, 2020, 27 (6): 1- 6. doi: 10.12132/ISSN.1673-5048.2020.0227
13	YOU X, YAN X P, LIU J L, et al. Adaptive neural sliding mode control for heterogeneous ship formation keeping considering uncertain dynamics and disturbances[J]. Ocean Engineering, 2022, 263, 112268. doi: 10.1016/j.oceaneng.2022.112268
14	孙海文, 王溪野, 王兆辰, 等. 海上异构无人集群协同运用发展概况及启示[J]. 水下无人系统学报, 2024, 32 (2): 275- 281. doi: 10.11993/j.issn.2096-3920.2024-0032
117	CUI R X, YANG C G, LI Y, et al. Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2017, 47 (6): 1019- 1029.
118	ZHANG K Q, YANG Z R, BAŞAR T. Multi-agent reinforcement learning: a selective overview of theories and algorithms[J]. Handbook of Reinforcement Learning and Control, 2021, 325, 321- 384.
119	ZHAO Y J, MA Y, HU S L. USV formation and path-following control via deep reinforcement learning with random braking[J]. IEEE Trans. on Neural Networks and Learning Systems, 2021, 32 (12): 5468- 5478. doi: 10.1109/TNNLS.2021.3068762
120	HE J Y, XI M, WEN J B, et al. Marl-based AUV formation for underwater intelligent autonomous transport systems supported by 6G network[J]. IEEE Trans. on Intelligent Transportation Systems, 2024, 26 (10): 17339- 17348.
121	FANG Z, JIANG D, HUANG J, et al. Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning[J]. Ocean Engineering, 2022, 262, 112182. doi: 10.1016/j.oceaneng.2022.112182
122	WANG Z K, ZHANG L J. Distributed optimal formation tracking control based on reinforcement learning for underactuated AUVs with asymmetric constraints[J]. Ocean Engineering, 2023, 280, 114491. doi: 10.1016/j.oceaneng.2023.114491
123	WANG Z, ZHANG L, ZHU Z. Game-based distributed optimal formation tracking control of underactuated AUVs based on reinforcement learning[J]. Ocean Engineering, 2023, 287, 115879. doi: 10.1016/j.oceaneng.2023.115879
124	LIU Z W, HOU H Z, WANG Y W. Formation-containment control of multiple underactuated surface vessels with sampling communication via hierarchical sliding mode approach[J]. ISA Transactions, 2022, 124, 458- 467. doi: 10.1016/j.isatra.2019.12.003
125	OGREN P, EGERSTEDT M, HU X. A control Lyapunov function approach to multi-agent coordination[C]// Proc. of the 40th IEEE Conference on Decision and Control, 2001: 1150−1155.
126	WANG H Q, DONG J X. Neural network-based hierarchical fault-tolerant affine formation control for heterogeneous nonlinear multi-agent systems[J]. IEEE Trans. on Intelligent Transportation Systems, 2023, 25 (5): 4503- 4511.
127	SEPULCHRE R, PALEY D A, LEONARD N E. Stabilization of planar collective motion: all-to-all communication[J]. IEEE Trans. on Automatic Control, 2007, 52 (5): 811- 824. doi: 10.1109/TAC.2007.898077
128	GODSIL C, ROYLE G F. Algebraic graph theory[M]. Berlin: Springer Science and Business Media, 2001.
14	SUN H W, WANG X Y, WANG Z C, et al. Development overview and inspiration of collaborative application of heterogeneous unmanned cluster’s at sea[J]. Journal of Unmanned Undersea Systems, 2024, 32 (2): 275- 281. doi: 10.11993/j.issn.2096-3920.2024-0032
15	YOON S, DO H, KIM J. Collaborative mission and route planning of multi-vehicle systems for autonomous search in marine environment[J]. International Journal of Control, Automation and Systems, 2020, 18 (3): 546- 555.
16	TIAN Y P, LIU C L. Consensus of multi-agent systems with diverse input and communication delays[J]. IEEE Trans. on Automatic Control, 2008, 53 (9): 2122- 2128. doi: 10.1109/TAC.2008.930184
17	DONG S Q, LIU M Q, DONG S L, et al. Hierarchical heterogeneous multi-agent cross-domain search method based on deep reinforcement learning[J]. IEEE Trans. on Intelligent Transportation Systems, 2024, 25 (11): 18872- 18883. doi: 10.1109/TITS.2024.3417698
18	WEI W, WANG J J, FANG Z R, et al. 3U: joint design of UAV-USV-UUV networks for cooperative target hunting[J]. IEEE Trans. on Vehicular Technology, 2022, 72 (3): 4085- 4090.
19	ZHANG G Q, CAO Q, HUANG C F, et al. Adaptive neural quantized formation control for heterogeneous underactuated ships with the MVs guidance[J]. Ocean Engineering, 2024, 294, 116760. doi: 10.1016/j.oceaneng.2024.116760
20	LI S F, ZHANG F M, WANG S X, et al. Constructing the three-dimensional structure of an anticyclonic eddy with the optimal configuration of an underwater glider network[J]. Applied Ocean Research, 2020, 95, 101893. doi: 10.1016/j.apor.2019.101893
21	王广旭, 蔡伟, 李永琪, 等. 含不确定动态观测的异构无人舰艇系统固定时间编队控制[J]. 电光与控制, 2025, 32 (5): 103- 110. doi: 10.3969/j.issn.1671-637X.2025.05.0017
	WANG G X, CAI W, LI Y Q, et al. Fixed-time formation control for heterogeneous unmanned surface vehicles with uncertain dynamic observations[J]. Electronics Optics & Control, 2025, 32 (5): 103- 110. doi: 10.3969/j.issn.1671-637X.2025.05.0017
22	周安琪. 多UUV/USV路径规划及协同编队控制[D]. 南京: 南京理工大学, 2021.
	ZHOU A Q. Multiple UUV/USV path planning and formation control[D]. Nanjing: Nanjing University of Science & Technology, 2021.
23	WU Y, LOW K H, LV C. Cooperative path planning for heterogeneous unmanned vehicles in a search-and-track mission aiming at an underwater target[J]. IEEE Trans. on Vehicular Technology, 2020, 69 (6): 6782- 6787. doi: 10.1109/TVT.2020.2991983
129	NAJA M, SHAIKHOLESLAM F. Graph theoretical methods to study controllability and leader selection for dead-time systems[J]. Transactions on Combinatorics, 2013, 2 (4): 25- 36.
130	FAX J A, MURRAY R M. Information flow and cooperative control of vehicle formations[J]. IEEE Trans. on Automatic Control, 2004, 49 (9): 1465- 1476. doi: 10.1109/TAC.2004.834433
131	LAFFERRIERE G, CAUGHMAN J, WILLIAMS A. Graph theoretic methods in the stability of vehicle formations[C]// Proc. of the American Control Conference, 2004: 3729−3734.
132	LIN Z, FRANCIS B, MAGGIORE M. Necessary and sufficient graphical conditions for formation control of unicycles[J]. IEEE Trans. on Automatic Control, 2005, 50 (1): 121- 127. doi: 10.1109/TAC.2004.841121
133	AYAZ M, AMMAD-UDDIN M, SHARIF Z, et al. A hybrid data collection scheme to achieve load balancing for underwater sensor networks[J]. Journal of King Saud University-Computer and Information Sciences, 2023, 35 (3): 74- 86. doi: 10.1016/j.jksuci.2023.02.006
134	GUSSEN C M, DINIZ P S, CAMPOS M L, et al. A survey of underwater wireless communication technologies[J]. Communications of the Association for Information Systems, 2016, 31 (1): 242- 255. doi: 10.14209/jcis.2016.22
135	CAMPAGNARO F, FRANCESCON R, CASARI P, et al. Multimodal underwater networks: recent advances and a look ahead[C]// Proc. of the 12th International Conference on Underwater Networks & Systems, 2017.
136	王童豪, 彭星光, 胡浩, 等. 海上有人/无人协同系统及其关键技术综述[J]. 兵工学报, 2024, 45 (10): 3317- 3340. doi: 10.12382/bgxb.2024.0327
	WANG T H, PENG X G, HU H, et al. Maritime manned/unmanned collaborative systems and key technologies: a survey[J]. Acta Armamentarii, 2024, 45 (10): 3317- 3340. doi: 10.12382/bgxb.2024.0327
137	HUANG J G, WANG H, HE C B, et al. Underwater acoustic communication and the general performance evaluation criteria[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19 (8): 951- 971.
138	ZENG Z Q, FU S, ZHANG H H, et al. A survey of underwater optical wireless communications[J]. IEEE Communications Surveys and Tutorials, 2016, 19 (1): 204- 238.
139	GABRIEL C, KHALIGHI M A, BOURENNANE S, et al. Monte-Carlo-based channel characterization for underwater optical communication systems[J]. Journal of Optical Communications and Networking, 2012, 5 (1): 1- 12.
24	高永琪, 马威强, 张林森, 等. 分布式多AUV协同搜索方法[J]. 系统工程与电子技术, 2022, 44 (5): 1670- 1676. doi: 10.12305/j.issn.1001-506X.2022.05.29
	GAO Y Q, MA W Q, ZHANG L S, et al. Distributed multi-AUVs cooperative search method[J]. Systems Engineering and Electronics, 2022, 44 (5): 1670- 1676. doi: 10.12305/j.issn.1001-506X.2022.05.29
25	张杨, 周作鹏, 田燕妮. 基于分布式概念的水下无人作战系统发展研究[J]. 舰船科学技术, 2023, 45 (21): 119- 124. doi: 10.3404/j.issn.1672-7649.2023.21.022
	ZHANG Y, ZHOU Z P, TIAN Y N. Development of distributed underwater unmanned combat[J]. Ship Science and Technology, 2023, 45 (21): 119- 124. doi: 10.3404/j.issn.1672-7649.2023.21.022
26	SMITH R N, PEREIRA A, CHAO Y, et al. Autonomous underwater vehicle trajectory design coupled with predictive ocean models: a case study[C]// Proc. of the IEEE International Conference on Robotics and Automation, 2010: 4770−4777.
27	MA X J, WANG Y H, LI S, et al. Formation control of discrete-time nonlinear multi-glider systems for both leader–follower and no-leader scenarios under switching topology: cases for a fleet consisting of a wave glider and multiple underwater gliders[J]. Ocean Engineering, 2023, 276, 114003. doi: 10.1016/j.oceaneng.2023.114003
28	毛柳伟, 杜度, 李杨. 水下滑翔机集群应用现状与关键技术展望[J]. 舰船科学技术, 2020, 42 (12): 13- 20. doi: 10.3404/j.issn.1672-7649.2020.12.003
	MAO L W, DU D, LI Y. Application status and key technology prospect of underwater glider cluster[J]. Ship Science and Technology, 2020, 42 (12): 13- 20. doi: 10.3404/j.issn.1672-7649.2020.12.003
29	CURTIN T B, BELLINGHAM J G, CATIPOVIC J, et al. Autonomous oceanographic sampling networks[J]. Oceanography, 1993, 6 (3): 86- 94. doi: 10.5670/oceanog.1993.03
30	RAMP S R, DAVIS R E, LEONARD N E, et al. Preparing to predict: the second autonomous ocean sampling network （AOSN-II） experiment in the MONTEREY bay[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2009, 56 (3/5): 68- 86. doi: 10.1016/j.dsr2.2008.08.013
31	SHU Y Q, CHEN J, LI S, et al. Field-observation for an anticyclonic mesoscale eddy consisted of twelve gliders and sixty-two expendable probes in the northern south china sea during summer 2017[J]. Science China Earth Sciences, 2019, 62, 451- 458. doi: 10.1007/s11430-018-9239-0
140	WANG Y H, CUI J, ZHAO B D, et al. Observation and communication platform design of USV for marine environmental parameters[C]// Proc. of the Global Oceans 2020: Singapore–US Gulf Coast, 2020.
141	YAN Z P, XU D, CHEN T, et al. Formation control of unmanned underwater vehicles using local sensing means in absence of follower position information[J]. International Journal of Advanced Robotic Systems, 2021, 18(1): 1729881420986745.
142	景锐洁. 异构AUV编队系统分布式控制研究[D]. 哈尔滨: 哈尔滨工程大学, 2023.
	JING R J. Research on distributed control of heterogeneous AUV formation systems[D]. Harbin: Harbin Engineering University, 2023.
143	SURYENDU C, SUBUDHI B. Formation control of multiple autonomous underwater vehicles under communication delays[J]. IEEE Trans. on Circuits and Systems II: Express Briefs, 2020, 67 (12): 3182- 3186.
144	SURYENDU C, SUBUDHI B. Modified constrained adaptive formation control scheme for autonomous underwater vehicles under communication delays[J]. IET Cyber-Systems and Robotics, 2020, 2 (1): 22- 30. doi: 10.1049/iet-csr.2019.0027
145	LI J, DU J L, LEWIS F L. Distributed three-dimension time-varying formation control with prescribed performance for multiple underactuated autonomous underwater vehicles[J]. International Journal of Robust and Nonlinear Control, 2021, 31 (13): 6272- 6287. doi: 10.1002/rnc.5611
146	LI J, TIAN Z Y, ZHANG H H. Discrete-time AUV formation control with leader-following consensus under time-varying delays[J]. Ocean Engineering, 2023, 286, 115678. doi: 10.1016/j.oceaneng.2023.115678
147	DU J L, LI J, LEWIS F L. Distributed 3-D time-varying formation control of underactuated AUVS with communication delays based on data-driven state predictor[J]. IEEE Trans. on Industrial Informatics, 2022, 19 (5): 6963- 6971.
148	YAMCHI M H, ESFANJANI R M. Distributed predictive formation control of networked mobile robots subject to communication delay[J]. Robotics and Autonomous Systems, 2017, 91, 194- 207. doi: 10.1016/j.robot.2017.01.005
149	GUPTA C, KARN C K, BANYAL R. Delay minimising depth-based routing for multi-sink underwater wireless sensor networks[J]. International Journal of Sensor Networks, 2018, 27 (2): 85- 94. doi: 10.1504/IJSNET.2018.092627
150	JIANG S M. State-of-the-art medium access control （MAC） protocols for underwater acoustic networks: a survey based on a mac reference model[J]. IEEE Communications Surveys and Tutorials, 2017, 20 (1): 96- 131.
32	QIU C H, MAO H B, LIU H L, et al. Deformation of a warm eddy in the northern south china sea[J]. Journal of Geophysical Research: Oceans, 2019, 124 (8): 5551- 5564.
33	QIU C H, YANG Z H, WANG D X, et al. The enhancement of submesoscale ageostrophic motion on the mesoscale eddies in the south china sea[J]. Journal of Geophysical Research: Oceans, 2022, 127 (9): e2022JC018736.
34	天津大学新闻网. 天大“海燕”水下滑翔机助力世界最大规模中尺度漩涡观测项目[EB/OL]. https://news.tju.edu.cn/info/1012/34113.html.
	Tianjin University News. Tianjin University’s “Haiyan” underwater glider supports the world’s largest mesoscale eddy observation project[EB/OL]. https://news.tju.edu.cn/info/1012/34113.html.
35	沈阳自动化研究所. 沈阳自动化所举行空海一体化立体协同观测联合试验[EB/OL]. [2025-01-05]. https://www.cas.cn/yx/201806/t201806254655803.shtml.
	Shenyang Institute of Automation. SIA conducts joint experiment on integrated air-sea stereoscopic cooperative observation[EB/OL]. [2025-01-05]. https://www.cas.cn/yx/201806/t201806254655803.shtml.
36	中国交通建设集团有限公司粤港澳大湾区创新研究院. 海洋观测利器-智能敏捷海洋立体观测系统[EB/OL]. [2025-01-05]. https://www.cccchzmb.com/haiyangguanceliqi-zhinengminjiehaiyanglitiguancexitong/.
	China Communications Construction Company Guangdong-Hong Kong-Macao Greater Bay Area Innovation Research Institute. A cutting-edge tool for marine observation-intelligent agile ocean stereoscopic observation system[EB/OL]. [2025-01-05]. https://www.ccccltdgb.com/haiyangguanceliqi-zhinengminjiehaiyanglitiguancexitong/.
37	杨绍琼, 李元昊, 孙通帅, 等. “海燕”号谱系化水下滑翔机技术发展与应用[J]. 水下无人系统学报, 2023, 31 (1): 68- 85.
	YANG S Q, LI Y H, SUN T S, et al. Development and application of petrel serialized underwater glider technologies[J]. Journal of Unmanned Undersea Systems, 2023, 31 (1): 68- 85.
38	YANG Y, WANG S X, WU Z L, et al. Motion planning for multi-hug formation in an environment with obstacles[J]. Ocean Engineering, 2011, 38 (17/18): 2262- 2269. doi: 10.1016/j.oceaneng.2011.10.008
39	XUE D Y, WU Z L, WANG Y H, et al. Coordinate control, motion optimization and sea experiment of a fleet of petrel-II gliders[J]. Chinese Journal of Mechanical Engineering, 2018, 26 (1): 311- 315.
151	刘祥玲. 复杂环境下无人潜航器编队协调控制方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
	LIU X L. Research on coordination control of multiple UUVs in complex environment[D]. Harbin: Harbin Engineering University, 2019.
152	CHEN S, HO D W. Consensus control for multiple AUVs under imperfect information caused by communication faults[J]. Information Sciences, 2016, 370, 565- 577.
153	HU Z L, MA C, ZHANG L X, et al. Formation control of impulsive networked autonomous underwater vehicles under fixed and switching topologies[J]. Neurocomputing, 2015, 147, 291- 298. doi: 10.1016/j.neucom.2014.06.060
154	HU C Q, FU L, YANG Y Y. Cooperative navigation and control for surface-underwater autonomous marine vehicles[C]// Proc. of the IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference, 2017: 589−592.
155	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. doi: 10.13195/j.kzyjc.2022.1424
	YAN J, XU L, CAO W Q, et al. Design of formation control algorithm for multiple autonomous underwater vehicles based on deep reinforcement learning[J]. Control and Decision, 2023, 38 (5): 1457- 1463. doi: 10.13195/j.kzyjc.2022.1424
156	SEILER P, SENGUPTA R. Analysis of communication losses in vehicle control problems[C]// Proc. of the American Control Conference, 2001: 1491−1496.
157	王朝阳, 刁维卿, 徐博, 等. 自适应量测-通信联合框架下基于RMPC的AUV编队控制[J]. 控制与决策, 2024, 40 (1): 271- 278. doi: 10.13195/j.kzyjc.2024.0010
	WANG Z Y, DIAO W Q, XU B, et al. Distributed AUV formation control based on RMPC in adaptive measure-communication union framework[J]. Control and Decision, 2024, 40 (1): 271- 278. doi: 10.13195/j.kzyjc.2024.0010
158	丁文俊, 张国宗, 刘海旻, 等. 面向海流扰动和通信时延的欠驱动AUV编队跟踪控制[J]. 兵工学报, 2024, 45 (1): 184- 196. doi: 10.12382/bgxb.2023.0417
	DING W J, ZHANG G Z, LIU H M, et al. Tracking control of underactuated autonomous underwater vehicle formation under current disturbance and communication delay[J]. Acta Armamentarii, 2024, 45 (1): 184- 196. doi: 10.12382/bgxb.2023.0417
40	KE C, CHEN H F, XIE L. Cross-domain fixed-time formation control for an air-sea heterogeneous unmanned system with disturbances[J]. Journal of Marine Science and Engineering, 2023, 11 (7): 1336. doi: 10.3390/jmse11071336
41	BRANCH A, FLEXAS M M, CLAUS B, et al. Front delineation and tracking with multiple underwater vehicles[J]. Journal of Field Robotics, 2019, 36 (3): 568- 586. doi: 10.1002/rob.21853
42	WILLCOX S, GOLDBERG D, VAGANAY J, et al. Multi-vehicle cooperative navigation and autonomy with the bluefin cadre system[C]// Proc. of the International Federation of Automatic Control Conference, 2006: 20−22.
43	邱子越. 无人艇将颠覆未来海战模式[EB/OL]. [2025-01-05]. http://www.81.cn/gfbmap/content/2019-02/13/content_227165.htm.
	QIU Z Y. Unmanned surface vessels to revolutionize future naval warfare models[EB/OL]. [2025-01-05]. http://www.81.cn/gfbmap/content/2019-02/13/content_227165.htm.
44	MUMFORD R. Ocean2020-European defence fund’s first research initiative[J]. Microwave Journal, 2018, 61 (2): 69.
45	LI Y, MA T, CHEN P Y, et al. Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation[J]. Ocean Engineering, 2017, 133, 107- 115. doi: 10.1016/j.oceaneng.2017.01.026
46	丁文俊, 柴亚军, 杨宇贤, 等. 基于空海异构无人平台的水下目标搜索与跟踪[J]. 水下无人系统学报, 2024, 32 (2): 237- 249. doi: 10.11993/j.issn.2096-3920.2024-0037
	DING W J, CHAI Y J, YANG Y X, et al. Underwater target search and tracking based on air-sea heterogeneous unmanned platform[J]. Journal of Unmanned Undersea Systems, 2024, 32 (2): 237- 249. doi: 10.11993/j.issn.2096-3920.2024-0037
47	王宁, 梁晓龙, 张佳强, 等. 跨域无人集群协同反潜搜索方法研究[J]. 系统仿真学报, 2024, 36 (4): 817- 824. doi: 10.16182/j.issn1004731x.joss.23-0073
	WANG N, LIANG X L, ZHANG J Q, et al. Research on cross-domain unmanned swarm cooperative anti-submarine search method[J]. Journal of System Simulation, 2024, 36 (4): 817- 824. doi: 10.16182/j.issn1004731x.joss.23-0073
48	吴迪. 分布式多水下无人航行器搜捕任务协调方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2015.
159	HUANG Y Y, LI W, NING J, et al. Formation control for UAV-USVs heterogeneous system with collision avoidance performance[J]. Journal of Marine Science and Engineering, 2023, 11 (12): 2332. doi: 10.3390/jmse11122332
160	ER M J, GONG H B, LIU Y, et al. Intelligent trajectory tracking and formation control of underactuated autonomous underwater vehicles: a critical review[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023, 54 (1): 543- 555.
161	PAN C, PENG Z H, LIU L, et al. Data-driven distributed formation control of under-actuated unmanned surface vehicles with collision avoidance via model-based deep reinforcement learning[J]. Ocean Engineering, 2023, 267, 113166. doi: 10.1016/j.oceaneng.2022.113166
162	WANG H B, LUO Q D, LI N, et al. Data-driven model free formation control for multi-USV system in complex marine environments[J]. International Journal of Control, Automation and Systems, 2022, 20 (11): 3666- 3677.
163	CHAO C Z L, TAN H R, ZHANG X M, et al. Data-driven adaptive formation control based on preview mechanism for networked multi-robot systems with communication delays[J]. Neurocomputing, 2025, 620, 129151. doi: 10.1016/j.neucom.2024.129151
164	LV G H, PENG Z H, LIU L, et al. Barrier-certified distributed model predictive control of under-actuated autonomous surface vehicles via neurodynamic optimization[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2022, 53 (1): 563- 575.
165	XU J, WANG Y, XING W, et al. Model-free finite-time formation containment control of underactuated UUVs subject to input delays and unknown interaction information[J]. Ocean Engineering, 2023, 283, 115054. doi: 10.1016/j.oceaneng.2023.115054
166	ZHANG D, N’DOYE I, BALLAL T, et al. Localization and tracking control using hybrid acoustic-optical communication for autonomous underwater vehicles[J]. IEEE Internet of Things Journal, 2020, 7 (10): 10048- 10060. doi: 10.1109/JIOT.2020.2995799
167	ISLAM K Y, AHMAD I, HABIBI D, et al. Green underwater wireless communications using hybrid optical-acoustic technologies[J]. IEEE Access, 2021, 9, 85109- 85123. doi: 10.1109/ACCESS.2021.3088467
168	GAUNI S, MANIMEGALAI C, KRISHNAN K M, et al. Design and analysis of co-operative acoustic and optical hybrid communication for underwater communication[J]. Wireless Personal Communications, 2021, 117, 561- 575. doi: 10.1007/s11277-020-07883-1
169	DIAMANT R, CAMPAGNARO F, DE GRAZIA M D F, et al. On the relationship between the underwater acoustic and optical channels[J]. IEEE Trans. on Wireless Communications, 2017, 16 (12): 8037- 8051. doi: 10.1109/TWC.2017.2756055
170	MILLAN P, ORIHUELA L, JURADO I, et al. Formation control of autonomous underwater vehicles subject to communication delays[J]. IEEE Trans. on Control Systems Technology, 2013, 22 (2): 770- 777.
48	WU D. Research on cooperative strategies of distributed multiple AUVs for searching and hunting[D]. Harbin: Harbin Engineering University, 2015.
49	王景璟, 魏维, 王永越, 等. 水下自主潜航器集群协同围捕技术[J]. 指挥与控制学报, 2023, 9 (6): 637- 650. doi: 10.3969/j.issn.2096-0204.2023.06.0637
	WANG J J, WEI W, WANG Y Y, et al. Autonomous underwater vehicle swarm collaborative hunting technologies[J]. Journal of Command and Control, 2023, 9 (6): 637- 650. doi: 10.3969/j.issn.2096-0204.2023.06.0637
50	CAO X, YU H C, SUN H B. Dynamic task assignment for multi-AUV cooperative hunting[J]. Intelligent Automation and Soft Computing, 2019, 25 (1): 25- 34.
51	CUI Y F, XU J, XING W, et al. Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with actuator saturation[J]. Ocean Engineering, 2022, 266: 113026.
52	KALWA J. Final results of the european project GREX: coordination and control of cooperating marine robots[J]. IFAC Proceedings Volumes, 2010, 43 (16): 181- 186. doi: 10.3182/20100906-3-IT-2019.00033
53	KALWA J. The grex-project: coordination and control of cooperating heterogeneous unmanned systems in uncertain environments[C]// Proc. of the OCEANS 2009-EUROPE, 2009: 385−398.
54	NI J J, YANG L, WU L Y, et al. An improved spinal neural system-based approach for heterogeneous AUVs cooperative hunting[J]. International Journal of Fuzzy Systems, 2018, 20 (2): 672- 686. doi: 10.1007/s40815-017-0395-x
55	张朦朦. 基于分层强化学习的MAUVS围捕策略研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
	ZHANG M M. Research on mauvs capture strategy based on hierarchical reinforcement learning[D]. Harbin: Harbin Engineering University, 2018.
56	CHEN M Z, ZHU D Q. A novel cooperative hunting algorithm for inhomogeneous multiple autonomous underwater vehicles[J]. IEEE Access, 2018, 6, 7818- 7828. doi: 10.1109/ACCESS.2018.2801857
57	王杰. 多AUV围捕决策与行为协同控制研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
171	YAN Z P, YANG Z W, YUE L D, et al. Discrete-time coordinated control of leader-following multiple AUVs under switching topologies and communication delays[J]. Ocean Engineering, 2019, 172, 361- 372. doi: 10.1016/j.oceaneng.2018.12.018
172	SHAN Q H, CHEN Z Z, LI T H, et al. Consensus of multi-agent systems with impulsive perturbations and time-varying delays by dynamic delay interval method[J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 78, 104890. doi: 10.1016/j.cnsns.2019.104890
173	WEI H L, LIU C X, SHI Y. A robust distributed MPC framework for multi-agent consensus with communication delays[J]. IEEE Trans. on Automatic Control, 2024, 69 (11): 7418- 7432. doi: 10.1109/TAC.2024.3389075
174	LI J, DU J L, LI Y P, et al. Distributed robust prescribed performance 3-D time-varying formation control of underactuated AUVs under input saturations and communication delays[J]. IEEE Journal of Oceanic Engineering, 2023, 48 (3): 649- 662. doi: 10.1109/JOE.2023.3245671
175	PENG Z H, JIANG Y, WANG J. Event-triggered dynamic surface control of an underactuated autonomous surface vehicle for target enclosing[J]. IEEE Trans. on Industrial Electronics, 2020, 68 (4): 3402- 3412.
176	KIM J H, YOO S J. Adaptive event-triggered control strategy for ensuring predefined three-dimensional tracking performance of uncertain nonlinear underactuated underwater vehicles[J]. Mathematics, 2021, 9 (2): 137. doi: 10.3390/math9020137
177	THUYEN N A, THANH P N N, ANH H P H. Distributed event-triggered adaptive finite-time formation control for multiple under-actuated AUVs using command filtered backstepping technique with prescribed performance[J]. European Journal of Control, 2025, 82, 101183. doi: 10.1016/j.ejcon.2025.101183
178	FENG Z G, YAO S. Dynamic event-triggered active disturbance rejection formation control for constrained underactuated AUVS[J]. IEEE/CAA Journal of Automatica Sinica, 2025, 12 (2): 460- 462. doi: 10.1109/JAS.2024.124617
179	ZHOU X T, XING X Y, QIN H D, et al. Communication-performance trade-off formation control for NAUVs: an interleaved event-triggered strategy[J]. Nonlinear Dynamics, 2025, 113, 8713- 8739. doi: 10.1007/s11071-024-10744-2
180	COSTANZI R, FENUCCI D, MANZARI V, et al. Interoperability among unmanned maritime vehicles: review and first in-field experimentation[J]. Frontiers in Robotics and AI, 2020, 7, 91. doi: 10.3389/frobt.2020.00091
181	LÓPEZ D S, MORENO G, CORDERO J, et al. Search and rescue robotics-from theory to practice[M]. London: Intech, 2017.
57	WANG J. Research on multi-AUVS hunting decision-making and behavior cooperative control[D]. Harbin: Harbin Engineering University, 2018.
58	JON H. Navy’s project overmatch steams ahead at RIMPAC[EB/OL]. [2025-01-05]. https://defensescoop.com/2024/08/15/navy-project-overmatch-rimpac-2024-steams-ahead/.
59	Commander, U. S. 3rd Fleet Public Affairs. Inaugural unmanned battle problem 21 to begin april 19[EB/OL]. [2025-01-05]. https://www.c3f.navy.mil/News/Article/2786689/inaugural-unmanned-battle-problem-21-to-begin-april-19/.
60	Commander, U. S. 3rd Fleet Public Affairs. Integrated battle problem 23.1 kicks off[EB/OL]. [2025-01-05]. https://www.pacom.mil/Media/NEWS/News-Article-View/Article/3388585/integrated-battle-problem-231-kicks-off/.
61	Commander, U. S. 3rd Fleet Public Affairs. Integrated battle problem 23.2concludes as unmanned surface vessels return to homeport[EB/OL]. [2025-01-05]. https://www.navy.mil/Press-Office/News-Stories/Article/3645411/integrated-battle-problem-232-concludes-as-unmanned-surface-vessels-return-to-h/.
62	Commander, U. S. 3rd Fleet Public Affairs. Integrated battle problem 24.1 concludes[EB/OL]. [2025-01-05]. https://www.dvidshub.net/news/466700/integrated-battle-problem-241-concludes.
63	宋逸哲, 刘妹琴, 董山玲, 等. 弱通信条件下多水下自主航行器分布式编队轨迹规划方法[J]. 控制与决策, 2024, 40 (1): 71- 79.
	SONG Y Z, LIU M Q, DONG S L, et al. Distributed formation trajectory planning method for multiple autonomous underwater vehicles with limited communication[J]. Control and Decision, 2024, 40 (1): 71- 79.
64	YANG Y, XIAO Y, LI T S. A survey of autonomous underwater vehicle formation: performance, formation control, and communication capability[J]. IEEE Communications Surveys and Tutorials, 2021, 23 (2): 815- 841. doi: 10.1109/COMST.2021.3059998
65	王宏健, 熊伟, 陈子印, 等. 多自主水下航行器区域搜索与协同围捕方法研究[J]. 中国造船, 2010, 51 (2): 117- 125. doi: 10.3969/j.issn.1000-4882.2010.02.015
	WANG H J, XIONG W, CHEN Z Y, et al. Research on methods of region searching and cooperative hunting for autonomous underwater vehicles[J]. Shipbuilding of China, 2010, 51 (2): 117- 125. doi: 10.3969/j.issn.1000-4882.2010.02.015
66	闫敬, 张诗杭, 关新平, 等. 水下无人系统跨域协同控制: 研究进展与挑战[J]. 控制与决策, 2024, 40 (1): 7- 27. doi: 10.13195/j.kzyjc.2024.0283
182	KOTIS K, STAVRINOS S, KALLONIATIS C. Review on semantic modeling and simulation of cybersecurity and interoperability on the internet of underwater things[J]. Future Internet, 2022, 15 (1): 11. doi: 10.3390/fi15010011
183	KABANOV A, KRAMAR V. Marine internet of things platforms for interoperability of marine robotic agents: an overview of concepts and architectures[J]. Journal of Marine Science and Engineering, 2022, 10 (9): 1279. doi: 10.3390/jmse10091279
184	CHENG W L, ZHANG K, JIANG B. Distributed adaptive fixed-time fault-tolerant formation control for heterogeneous multiagent systems with a leader of unknown input[J]. IEEE Trans. on Cybernetics, 2022, 53 (11): 7285- 7294.
185	CHENG W L, ZHANG K, JIANG B. Hierarchical structure-based fixed-time optimal fault-tolerant time-varying output formation control for heterogeneous multiagent systems[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023, 53 (8): 4856- 4866.
186	ZHAO W, XIA Y, ZHAI D H, et al. Adaptive event-triggered coordination control of unknown autonomous underwater vehicles under communication link faults[J]. Automatica, 2023, 158, 111277. doi: 10.1016/j.automatica.2023.111277
187	GAO M, NIU Y C, SHENG L. Distributed fault-tolerant state estimation for a class of nonlinear systems over sensor networks with sensor faults and random link failures[J]. IEEE Systems Journal, 2022, 16 (4): 6328- 6337. doi: 10.1109/JSYST.2022.3142183
188	LIU S K, JIANG B, MAO Z H, et al. Adaptive fault-tolerant formation control of heterogeneous multi-agent systems under directed communication topology[J]. Sensors, 2022, 22 (16): 6212. doi: 10.3390/s22166212
189	ZHU B Y, LIANG H J, NIU B, et al. Observer-based reinforcement learning for optimal fault-tolerant consensus control of nonlinear multi-agent systems via a dynamic event-triggered mechanism[J]. Information Sciences, 2025, 689, 121350. doi: 10.1016/j.ins.2024.121350
190	TONG S C, ZHOU H D, LI Y M. Neural network event-triggered formation fault-tolerant control for nonlinear multiagent systems with actuator faults[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023, 53 (12): 7571- 7582.
66	YAN J, ZHANG S H, GUAN X P, et al. Cross domain cooperative control of underwater unmanned systems: research progresses and challenges[J]. Control and Decision, 2024, 40 (1): 7- 27. doi: 10.13195/j.kzyjc.2024.0283
67	YAMASHITA A, ARAI T, OTA J, et al. Motion planning of multiple mobile robots for cooperative manipulation and transportation[J]. IEEE Trans. on Robotics and Automation, 2003, 19 (2): 223- 237. doi: 10.1109/TRA.2003.809592
68	BURLUTSKIY N, TOUAHMI Y, LEE B H. Power efficient formation configuration for centralized leader–follower AUVs control[J]. Journal of Marine Science and Technology, 2012, 17 (3): 315- 329. doi: 10.1007/s00773-012-0167-0
69	GE X H, HAN Q L. Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism[J]. IEEE Trans. on Industrial Electronics, 2017, 64 (10): 8118- 8127. doi: 10.1109/TIE.2017.2701778
70	QU Z H, WANG J, HULL R A. Cooperative control of dynamical systems with application to autonomous vehicles[J]. IEEE Trans. on Automatic Control, 2008, 53 (4): 894- 911. doi: 10.1109/TAC.2008.920232
71	游旭, 刘佳仑, 马枫, 等. 异构船舶编队航行的研究现状与技术展望[J]. 哈尔滨工程大学学报, 2023, 44 (1): 11- 22. doi: 10.11990/jheu.202108016
	YOU X, LIU J L, MA F, et al. Analysis and strategy development for the navigation of heterogeneous ship formations[J]. Journal of Harbin Engineering University, 2023, 44 (1): 11- 22. doi: 10.11990/jheu.202108016
72	许真珍, 李一平, 封锡盛. 一个面向异构多UUV协作任务的分层式控制系统[J]. 机器人, 2008, 30 (2): 155- 159. doi: 10.3321/j.issn:1002-0446.2008.02.011
	XU Z Z, LI Y P, FENG X S. A hierarchical control system for heterogeneous multple UUV cooperation task[J]. Robot, 2008, 30 (2): 155- 159. doi: 10.3321/j.issn:1002-0446.2008.02.011
73	MARQUES F, SANTANA P, GUEDES M, et al. Online self-reconfigurable robot navigation in heterogeneous environments[C]// Proc. of the IEEE International Symposium on Industrial Electronics, 2013.
74	PINTO E, MARQUES F, MENDONÇA R, et al. An autonomous surface-aerial marsupial robotic team for riverine environmental monitoring: benefiting from coordinated aerial, underwater, and surface level perception[C]// Proc. of the IEEE International Conference on Robotics and Biomimetics, 2014: 443−450.
75	SHAO J, XIE G, WANG L. Leader-following formation control of multiple mobile vehicles[J]. IET Control Theory and Applications, 2007, 1 (2): 545- 552. doi: 10.1049/iet-cta:20050371
76	LEWIS M A, TAN K H. High precision formation control of mobile robots using virtual structures[J]. Autonomous Robots, 1997, 4 (4): 387- 403. doi: 10.1023/A:1008814708459
77	BALCH T, ARKIN R C. Behavior-based formation control for multirobot teams[J]. IEEE Trans. on Robotics and Automation, 1998, 14 (6): 926- 939. doi: 10.1109/70.736776
78	ZHAO W B, LIU H, LEWIS F L. Robust formation control for cooperative underactuated quadrotors via reinforcement learning[J]. IEEE Trans. on Neural Networks and Learning Systems, 2020, 32 (10): 4577- 4587.
79	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
80	CUI R X, GE S S, HOW B V E, et al. Leader–follower formation control of underactuated autonomous underwater vehicles[J]. Ocean Engineering, 2010, 37 (17/18): 1491- 1502. doi: 10.1016/j.oceaneng.2010.07.006
81	CAO X, GUO L Q. A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments[J]. International Journal of Advanced Robotic Systems, 2019, 16(4): 1729881419870664.
82	BECHLIOULIS C P, GIAGKAS F, KARRAS G C, et al. Robust formation control for multiple underwater vehicles[J]. Frontiers in Robotics and AI, 2019, 6, 90. doi: 10.3389/frobt.2019.00090
83	LIU Y Y, LIN X G, LIU Y C, et al. Robust anti-perturbation safety control of multi-heterogeneous unmanned vessels[J]. Ocean Engineering, 2024, 291, 116473. doi: 10.1016/j.oceaneng.2023.116473
84	高伟, 刘亚龙, 徐博. 基于双领航者的多AUV协同导航系统可观测性分析[J]. 系统工程与电子技术, 2013, 35 (11): 2370- 2375. doi: 10.3969/j.issn.1001-506X.2013.11.23
	GAO W, LIU Y L, XU B. Observability analysis of cooperative navigation system for multiple AUV basedon two-leaders[J]. Systems Engineering and Electronics, 2013, 35 (11): 2370- 2375. doi: 10.3969/j.issn.1001-506X.2013.11.23
85	CAO W Q, YAN J, YANG X, et al. Communication-aware formation control of AUVs with model uncertainty and fading channel via integral reinforcement learning[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10 (1): 159- 176. doi: 10.1109/JAS.2023.123021
86	王振威, 刘凯, 郭健, 等. 一种基于领导-跟随策略的多无人机-多无人艇编队协同机制[J]. 航空学报, 2023, 44 (S2): 453- 468.
	WANG Z W, LIU K, GUO J, et al. A multi-UAVs and multi-USVs formation cooperative mechanism based on leader-follower strategy[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (S2): 453- 468.
87	郑海滨. 无人艇和无人机水空跨域协同编队技术研究与实现[D]. 武汉: 华中科技大学, 2022.

项目名称	机构	开展时间	平台类型	项目成果
AOSN	美国海军研究院	1990—2005	REMUS、Slocum、Spray	观测近海和沿海区域内各种重要海洋特征和现象^[29]
AOSN-II	美国海军研究院	1990—2005	REMUS、Slocum、Spray	大范围、长航时异构自主海洋调查^[30]
ASAP	美国海军研究院	2006	Slocum、Spray	利用多台UG进行高效的海洋参数采样
GO-SHIP	美国国家海洋和大气管理局	2007—	Slocum、Spray、Seaglider、AUV	实施定期重复的高分辨率海洋观测，以提供持续、基准级高质量的海洋数据
OOI	美国国家科学基金会	2013—	Slocum、REMUS-600	建立了全球最大海洋观测网络
Ocean of Energy	荷兰海洋能源研究所	2020—	Slocum、Seaglider	实现海洋环境监测和能量采集开发

项目名称	机构	开展时间	平台类型	项目成果
CADRE	Bluefin公司	2006	AUV、USV	验证并实现美海军UUV总规划中提出的水下搜索与调查、辅助通信/导航能力^[42]
Unmanned Warrior	英国海军	2016	UAV、USV、UUV	完成了编队区域搜索、战场情报收集等任务^[43]
OCEAN 2020	欧盟	2018	UAV、USV、UUV	优化编队协同搜索与区域覆盖，提升了监视、探测与态势感知综合能力^[44]

平台类型	项目名称	开展时间	项目成果
Sea Hunter、Sea Hawk、MANTAS T-38 Devil Ray、 MQ-9 Sea Guardian、RQ-20 PUMA、LRUSVs、 LDUUV、ALTIUS、Spyglass、T-12 Mantas、 Switchlade 600、Gutlass	IBP21	2021.4	初步实现空-海-潜无人系统集成和跨域指挥控制架构^[59]
	IBP23.1	2023.5	验证无人平台联合火力打击能力^[60]
	IBP23.2	2023.8	验证USV在长航时任务中的自主性和盟友海军的互操作性^[61]
	IBP24.1	2024.3	实战化测试反馈技术缺陷，加快无人系统实战化部署和战斗力升级^[62]

类别	协调性	自主性	反应性	容错性	通信负载
集中式	√	×	×	×	×
分布式	×	√	√	√	×
分层式	√	√	√	√	√

策略名称	优势	局限性	研究文献
领航-跟随法	简单，可扩展性	灵活性差，领航者依赖	文献[27, 83, 91−93]
虚拟结构法	整体性强，队形保持与反馈	通信依赖，适应性有限	文献[76, 95−98]
基于行为法	去中心化，适应性强	协调难度大，稳定性差	文献[77, 100−104]
基于NNs	逼近能力强，鲁棒性好	训练复杂，参数调节困难	文献[80, 109−116]
基于RL	自学习能力强，计算力强	奖励函数设计困难，稳定性差	文献[119−123]

Heterogeneous formation control technology for unmanned marine vehicles: current status, challenges and perspectives

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 87

Related Articles 3

Recommended Articles

Metrics

Comments

类型	典型应用装备	通信速率	优缺点	适用场景
水声通信	UUV	1~100 Kbps	通信距离远；带宽窄、传输速率慢、时延大、信道不稳定	深水、远距离通信
激光通信	UUV	10~20 Mbps（水下100 m）	通信容量大、链路延迟低；传播距离有限、方向性极强	近距离、高速通信
VHF通信	USV	<1 Mbps	通信容量大、速率高；带宽较低、易受气候和各种干扰影响	近距离通信
卫星通信	USV	<500 Kbps（L/S频段）	通信距离远、容量大、稳定性好；传输时延大、有回声效应和通信盲区	跨海域通信

[1]	Bohai DENG, Jinfa XU. Semi-physical simulation verification of attitude control for compound unmanned helicopter [J]. Systems Engineering and Electronics, 2025, 47(2): 608-620.
[2]	Jialong ZHANG, Di ZHAO, Pu ZHANG, Lei YE. Adaptive tracking control for UAV swarm with communication constraint [J]. Systems Engineering and Electronics, 2025, 47(10): 3401-3410.
[3]	JIN Jian, CHEN Bohan. Analysis on control strategies of CO2 partial pressure of manned spacecraft assemble with multi-cabins [J]. Systems Engineering and Electronics, 2018, 40(6): 1351-1357.