Research Status and Prospect of Multi-domain Cluster Distributed Intelligent Cooperative Autonomous Control Technology

doi:10.12382/bgxb.2024.0866

Abstract

Abstract:

With the wide application of intelligent collaborative algorithm and autonomous technology in the field of air,space and sea equipment,the multi-domain cluster distributed collaborative autonomous control technology has also been deeply studied,and significantly improved the degree of intelligent autonomy of unmanned equipment.Based on the development needs of distributed intelligent autonomous control technology of multi-domain cluster,this paper reviews the relevant references related to multi-domain cluster distributed intelligent autonomous control technology and the research status at home and abroad.The relevant strategies,key research directions and advanced methods of multi-domain cluster collaborative autonomous control technology are deeply analyzed.The relevant key technologies and methods are summarized,and the future development trend is discussed.This paper provides a reference for improving the collaborative autonomous control ability of unmanned cluster system.

Key words: multi-domain cluster distributed system, intelligent cooperation, autonomous control, cooperative control

GAO Zhifa, ZHOU Yu, YANG Hang, LAN Qing, LI Yuzhe, GAO Hui, ZHANG Zhenhua. Research Status and Prospect of Multi-domain Cluster Distributed Intelligent Cooperative Autonomous Control Technology[J]. Acta Armamentarii, 2024, 45(S2): 9-16.

Figures/Tables 5

Fig.1 Multi-domain cluster distributed warfare concept

Fig.2 Composition of distributed intelligent cooperative autonomous control of multi-domain cluster

Fig.3 Cluster control based on behavior policy

Fig.4 Cluster control based on virtual structure strategy

Fig.5 Cluster control based on leader-follower strategy

References 61

[1]	江碧涛, 温广辉, 周佳玲, 等. 智能无人集群系统跨域协同技术研究现状与展望[J]. 中国工程科学, 2024, 26(1):117-126. doi: 10.15302/J-SSCAE-2024.01.015
	JIANG B T, WEN G H, ZHOU J L, et al. Cross-domain cooperative technology of intelligent unmanned swarm systems:current status and prospects[J]. Strategic Study of CAE, 2024, 26(1):117-126. (in Chinese)
[2]	郭兴, 李擎, 姚其家, 等. 异构无人系统协同控制研究进展[J/OL]. 工程科学学报, 2024(2024-01-12)[2024-09-15].https://doi.org/10.13374/j.issn2095-9389.2024.01.12.001.
	GUO X, LI Q, YAO Q J, et al. Research progress for cooperative control of heterogeneous unmanned systems[J/OL]. Chinese Journal of Engineering, 2024(2024-01-12)[2024-09-15].https://doi.org/10.13374/j.issn2095-9389.2024.01.12.001. (in Chinese)
[3]	逯杰. 跨域联合,未来联合作战新趋势[N]. 解放军报, 2020-09-08(07)[2024-09-15].
	LU J. Cross-domain alliance:New trend of future joint operations[N]. PLA Daily, 2020-09-08(07)[2024-09-15]. (in Chinese)
[4]	DEMPSEY M E. Joint operational access concept[R]. Department of Defense, 2020.
[5]	李龙跃, 贾忠慧, 皮雳, 等. 美军杀伤网的概念内涵、发展现状与趋势[J/OL]. 航空兵器, 2024 (2024-08-15)[2024-09-15]. http://kns.cnki.net/kcms/detail/41.1228.TJ.20240815.1604.004.html.
	LI L Y, JIA Z H, PI L, et al. The concept connotation,development status and trend of US Army kill net[J/OL]. Aero Weapony, 2024 (2024-08-15)[2024-09-15]. http://kns.cnki.net/kcms/detail/41.1228.TJ.20240815.1604.004.html. (in Chinese)
[6]	李磊, 王彤, 蒋琪. 从美军2042年无人系统路线图看无人系统关键技术发展动向[J]. 无人系统技术, 2018, 1(4):79-84.
	LI L, WANG T, JIANG Q. Key technology develop trends of unmanned systems viewed from unmanned systems integrated roadmap 2017-2042[J]. Unmanned Systems Technology. 2018, 1(4):79-84. (in Chinese)
[7]	何玉庆, 秦天一, 王楠. 跨域协同:无人系统技术发展和应用新趋势[J]. 无人系统技术, 2021, 4(4):13.
	HE Y Q, QING T Y, WANG N. Cross-domain collaboration:new trends in the development and application of unmanned systems technology[J]. Unmanned Systems Technology, 2021, 4(4):13. (in Chinese)
[8]	李姝, 裘昌利, 栾爽, 等. 美军无人系统发展规划研究综述[J]. 无人系统技术, 2023, 6(6):101-108.
	LI S, QIU C L, LUAN S, et al. Review on unmanned systems integrated roadmap by DoD[J]. Unmanned Systems Technology, 2023, 6(6):101-108. (in Chinese)
[9]	杨巍, 秦浩, 王佳, 等. 2021年世界军用无人系统领域发展综述[J]. 中国电子科学研究院学报, 2022, 17(4):368-373.
	YANG W, QIN H, WANG J, et al. Summary of the development of world military unmanned systems in 2021[J]. Journal of CAEIT, 2022, 17(4):368-373. (in Chinese)
[10]	张宝珍, 吴建龙. 美空军三位一体“试验旗”系列演习综述[J]. 计算机测量与控制, 2021, 29(12):1-7,12.
	ZHANG B Z, WU J L. Summary of US air force trinity “Test Flag” series exercises[J]. Computer Measurement & Control, 2021, 29(12):1-7,12. (in Chinese)
[11]	粟锋, 徐能武. 美国国防太空力量发展的动向及应对—基于对美国2020年《国防太空战略》的解读[J]. 国防科技, 2021, 42(3):91-97.
	SU F, XU N W. Trends and responses to the development of the U.S.defense space force-based on the interpretation of the《U.S.Defense Space Strategy 2020》[J]. National Defense Technology, 2021, 42(3):91-97. (in Chinese)
[12]	太阳谷. 美军联合全域作战探索路线浅析[J]. 军事文摘, 2020 (23):32-34.
	TAI Y G. A brief analysis of the exploration route of the United States military joint all-area operations[J]. Military Digest, 2020 (23):32-34. (in Chinese)
[13]	褚睿, 刘玮琦. “多域作战”的外军视角[N]. 解放军报, 2021-7-29(7)[2024-9-15].
	CHU R, LIU W Q. Foreign military perspective of "Multi-Domain Operations"[N]. PLA Daily, 2021-7-29(7)[2024-9-15]. (in Chinese)
[14]	European Defence Agency. Beyond 2040 - EDA analysis warns on future warfare trends and technology imperatives for European defence[EB/OL].(date-in-citation content-type="updated">2023-10-23)[2024-9-5]. https://defence-industry.eu/eda-enhancing-eu-military-capabilities-beyond-2040/.
[15]	Strategic Command. Multi-domain integration:Demystified[EB/OL].(2021-10-11)[2024-9-5]. https://stratcommand.blog.gov.uk/2021/10/11/multi-domain-integration-demystified/.
[16]	CHOUDHURY S, GUPTA J K, KOCHENDERFER M J, et al. Dynamic multi-robot task allocation under uncertainty and temporal constraints[J]. Autonomous Robots, 2022, 46(1):231-247.
[17]	CHENG M, LIU H, WEN G H, et al. Data-driven time-varying formation-containment control for a heterogeneous air-ground vehicle team subject to active leaders and switching topologies[J]. Automatica, 2023, 153:111029.
[18]	张鹏飞, 何印, 马振华, 等. 无人机集群协同控制技术综述[J]. 兵器装备工程学报, 2024, 45(4):1-9.
	ZHANG P F, HE Y, MA Z H, et al. Review on cooperative control technology of UAV swarm[J]. Journal of Ordnance Equipment Engineering, 2024, 45(4):1-9. (in Chinese)
[19]	贾永楠, 李擎. 多机器人编队控制研究进展[J]. 工程科学学报, 2018, 40(8):893-900.
	JIA Y N, LI Q. Research development of multi-robot formation control[J]. Chinese Journal of Engineering, 2018, 40(8):893-900. (in Chinese)
[20]	JIAO L, PENG Z, XI L, et al. Multi-agent coverage path planning via proximity interaction and cooperation[J]. IEEE Sensors Journal, 2022, 22(6):6196-6207.
[21]	ROPERTZ T, BERNS K, LI X, et al. Verification of behavior-based control systems in their physical environment[C]// Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen.Freiburg, Germany: University of Freiburg, 2016:128-137.
[22]	刘惟恒, 郑辛, 邓志红. 基于邻近行为观测方法的多无人机分布式自适应编队控制[J]. 中南大学学报, 2021, 3:784-795.
	LIU W H, ZHENG X, DENG Z H. Adaptive distributed formation maintenance for multiple UAVs:Exploiting proximity behavior observations[J]. Journal of Central South University. 2021,28,784-795. (in Chinese)
[23]	LEE G, CHWA D. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance[J]. Intelligent Service Robotics, 2017, 11(6):1-12.
[24]	DUAN H, XIN L, SHI Y. Homing pigeon-inspired autonomous navigation system for unmanned aerial vehicles[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(4):2218-2224.
[25]	FERIK E S. Biologically based control of a fleet of unmanned aerial vehicles facing multiple threats[J]. IEEE Access, 2020, 8:107146-107160.
[26]	QUAMAR M M, FERIK E S. Cooperative prey hunting for multi agent system designed using bio-inspired adaptation technique[C]// Proceedings of the 2023 International Conference on Control,Automation and Diagnosis (ICCAD). Rome,Italy: IEEE, 2023:1-6.
[27]	QUAMAR M M, FERIK E S. Control and coordination for swarm of UAVs under multi-predator attack[C]// Proceedings of the 2023 Systems and Information Engineering Design Symposium (SIEDS).Charlottesville,VA, 2023:96-101.
[28]	LEWIS M A, TAN K H. High precision formation control of mobile robots using virtual structures[J]. Autonomous robots, 1997, 4:387-403.
[29]	REN W, BEARD R. Decentralized scheme for spacecraft formation flying via the virtual structure approach[J]. Journal of Guidance Control & Dynamics, 2015, 27(1):1746-1751.
[30]	REN W, SORENSEN N. Distributed coordination architecture for multi-robot formation control[J]. Robotics and Autonomous Systems, 2008, 56(4):324-333.
[31]	DONG X W, LI Y F, LU C, et al. Time-varying formation tracking for UAV swarm systems with switching directed topologies[J]. IEEE transactions on neural networks and learning systems, 2018, 30(12):3674-3685.
[32]	LIU G P, ZHANG S. A survey on formation control of small satellites[J]. Proceedings of the IEEE, 2018, 106(3):440-457.
[33]	ZHOU D, WANG Z, SCHWAGER M. Agile coordination and assistive collision avoidance for quadrotor swarms using virtual structures[J]. IEEE Transactions on Robotics, 2018, 34(4):916-923.
[34]	NGUYEN M T. Energy-efficient mobile sensing in distributed multi-agent sensor networks[J]. Advances in Science,Technology and Engineering Systems Journal, 2017, 2(3):245-253.
[35]	LOW C B, SAN N Q. A flexible virtual structure formation keeping control for fixed-wing UAVs[C]// Proceedings of the 9th IEEE international conference on control and automation (ICCA).Santiago,Chile:IEEE, 2011:621-626.
[36]	ASKARI A, MORTAZAVI M, TALEBI H A. UAV formation control via the virtual structure approach[J]. Journal of Aerospace Engineering, 2015, 28(1):04014047.
[37]	GHAMRY K A, ZHANG Y. Formation control of multiple quadrotors based on leader-follower method[C]// Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS).Denver,CO,US:IEEE, 2015:1037-1042.
[38]	ROLDAO V, CUNHA R, CABECINHAS D, et al. A leader-following trajectory generator with application to quadrotor formation flight[J]. Robotics and Autonomous Systems, 2014, 62(10):1597-1609.
[39]	LIN J, MIAO Z, ZHONG H, et al. Adaptive image-based leader-follower formation control of mobile robots with visibility constraints[J]. IEEE Transactions on Industrial Electronics, 2020, 68(7):6010-6019.
[40]	ZHANG J, YAN J, ZHANG P. Multi-UAV formation control based on a novel back-stepping approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3):2437-2448.
[41]	GALZI D, SHTESSEL Y. UAV formations control using high order sliding modes[C]// Proceedings of the 2006 American Control Conference.Minneapolis,MN,US:IEEE, 2006:6.
[42]	KARTAL Y, SUBBARAO K, GANS N R, et al. Distributed backstepping based control of multiple UAV formation flight subject to time delays[J]. IET Control Theory & Applications, 2020, 14(12):1628-1638.
[43]	WANG J, HAN L, DONG X, et al. Distributed sliding mode control for time-varying formation tracking of multi-UAV system with a dynamic leader[J]. Aerospace Science and Technology, 2021, 111:106549.
[44]	WANG H, GUO D, LIANG X, et al. Adaptive vision-based leader-follower formation control of mobile robots[J]. IEEE Transactions on Industrial Electronics, 2017, 64(4):2893-2902.
[45]	MOROZOVA N S. Formation control and obstacle avoidance for multi-agent systems with dynamic topology[C]// Proceedings of the 2015 International Conference"Stability and Control Processes" in Memory of VI Zubov (SCP).Petersburg,Russia:IEEE, 2015:580-583.
[46]	OLFATI S R. Flocking for multi-agent dynamic systems:Algorithms and theory[J]. IEEE Transactions on automatic control, 2006, 51(3):401-420.
[47]	NGUYEN M T, LA H M, TEAGUE K A. Collaborative and compressed mobile sensing for data collection in distributed robotic networks[J]. IEEE Transactions on Control of Network Systems, 2017, 5(4):1729-1740.
[48]	NO T S, KIM Y, TAHK M J, et al. Cascade-type guidance law design for multiple-UAV formation keeping[J]. Aerospace Science and Technology, 2011, 15(6):431-439.
[49]	ALONSO M J, MONTIJANO E, NAGELI T, et al. Distributed multi-robot formation control in dynamic environments[J]. Autonomous Robots, 2019, 43:1079-1100.
[50]	ALONSO M J, MONTIJANO E, SCHWAGER M, et al. Distributed multi-robot formation control among obstacles:a geometric and optimization approach with consensus[C]// Proceedings of the 2016 IEEE international conference on robotics and automation (ICRA).Stockholm,Sweden:IEEE, 2016:5356-5363.
[51]	卫恒, 吕强, 刘扬, 等. 基于状态切换的分布式多机器人编队控制[J]. 兵工学报, 2019, 40(5):1103-1112. doi: 10.3969/j.issn.1000-1093.2019.05.024
	WEI H, LÜ Q, LIU Y, et al. State switching-based distributed multi-robot formation control[J]. Acta Armamentarii, 2019, 40(5):1103-1112. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.05.024
[52]	PAN Z, ZHANG C, XIA Y, et al. An improved artificial potential field method for path planning and formation control of the multi-UAV systems[J]. IEEE Transactions on Circuits and Systems II:Express Briefs, 2021, 69(3):1129-1133.
[53]	YU Y, GUO J, AHN C K, et al. Neural adaptive distributed formation control of nonlinear multi-UAVs with unmodeled dynamics[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 34(11):9555-9561.
[54]	SAIF A W A, ATAUR R M, ELFERIK S, et al. Multi-model fuzzy formation control of uav quadrotors[J]. Intelligent Automation and Soft Computing, 2021, 27(3):817-834.
[55]	HOANG V T, PHUNG M D, DINH T H, et al. Reconfigurable multi-UAV formation using angle-encoded PSO[C]// Proceedings of the 15th International Conference on Automation Science and Engineering (CASE).Vancouver,BC,Canada:IEEE, 2019:1670-1675.
[56]	DAI S L, LU K, JIN X. Fixed-time formation control of unicycle-type mobile robots with visibility and performance constraints[J]. IEEE Transactions on Industrial Electronics, 2020, 68(12):12615-12625.
[57]	LIY M, D S J, L K W, et al. Fuzzy adaptive fault tolerant time-varying formation control for nonholonomic multirobot systems with range constraints[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(6):3668-3679.
[58]	ZHOU H D, SUI S C, TONG S. Finite-time adaptive fuzzy prescribed performance formation control for high-order nonlinear multiagent systems based on event-triggered mechanism[J]. IEEE Transactions on Fuzzy Systems, 2022, 31(4):1229-1239.
[59]	TONG S, LI K, LI Y. Robust fuzzy adaptive finite-time control for high-order nonlinear systems with unmodeled dynamics[J]. IEEE Transactions on Fuzzy Systems, 2020, 29(6):1576-1589.
[60]	梁鸿涛, 王耀南, 华和安, 等. 无人集群系统深度强化学习控制研究进展[J]. 工程科学学报, 2024, 46(9):1521-1534.
	LIANG H T, WANG Y N, HUA H A, et al. Deep reinforcement learning to control an unmanned swarm system[J]. Chinese Journal of Engineering, 2024, 46(9):1521-1534. (in Chinese)
[61]	XU D, CHEN G. Autonomous and cooperative control of UAV cluster with multi-agent reinforcement learning[J]. The Aeronautical Journal, 2022, 126(1300):932-951.