无人机集群研究进展综述

doi:10.7527/S1000-6893.2019.23738

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

无人机集群研究进展综述

贾永楠¹, 田似营², 李擎¹

1. 北京科技大学自动化学院, 北京 100083;
2. 海鹰航空通用装备有限责任公司, 北京 100074

收稿日期:2019-12-17 修回日期:2019-12-20 出版日期:2020-06-30 发布日期:2019-12-26
通讯作者: 李擎 E-mail:liqing@ies.ustb.edu.cn
基金资助:
国家自然科学基金（61603362）；中央高校基本科研业务费专项资金（FRF-TP-19-031A2）

Recent development of unmanned aerial vehicle swarms

JIA Yongnan¹, TIAN Siying², LI Qing¹

1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. Hiwing Aviation General Equipment Co. Ltd. Beijing, Beijing 100074, China

Received:2019-12-17 Revised:2019-12-20 Online:2020-06-30 Published:2019-12-26
Supported by:
National Natural Science Foundation of China (61603362); Fundamental Research Funds for the Central Universities (FRF-TP-19-031A2)

摘要/Abstract

摘要： 集群行为是一种常见于自然界中鱼群、鸟群、蜂群等低等群居生物的集体行为，生物群中的个体仅依靠局部感知作用和简单的通信规则自主决定其运动状态，并且从简单的局部规则涌现出协同的整体行为。受此启发，提出了无人机集群作战的概念。无人机集群作战是指依靠大量低成本、速度快、适应能力强、易于携带和投射的无人机形成规模优势，从而取得战争的主动权。由于无人机集群技术的重要战略地位，中美俄等军事大国均开始重视无人机集群技术的持续发展。介绍了无人机集群的研究动机，从模型、协议和平台3个角度总结了研究方法，重点分析了几种典型作战模式以及涉及的若干关键技术。综上所述，集群技术在军事方面具有重要的应用价值，必将引领全新的作战模式。

关键词: 无人机, 集群技术, 大规模系统, 人机协同, 分布式控制

Abstract: Swarm as a typical collective behavior is omnipresent in animal kingdom, such as fish schooling, bird flocking, and bee swarming. In a colony, large-scale synchronous behavior emerges by virtue of local sensing and very simple communication rules. Inspired by these collective performances, the swarm of unmanned aerial vehicles is proposed as a new combat pattern. These unmanned aerial vehicles are characterized by large quantity, low cost, high speed, well adaptability, and convenient carried/launched mechanism, contributing to the scale advantage of UAVs and the possession of war initiative In recent years, many military powers, such as China, Russia, United States, have made great effort to the continuous development of swarm-related technology of unmanned aerial vehicles. To resolve the swarming problem of unmanned aerial vehicles, this paper introduces the research motivations of UAVs, summarizing the research approaches from modeling, control protocol, and execution platform. Besides, several classical combat modes and involved key technologies are discussed in detail. Above all, the swarm-related technology has bright application potential in the military area, leading to a brand new combat pattern.

Key words: unmanned aerial vehicles, swarming technology, large-scale system, man/unmanned cooperative control, distributed control

中图分类号:

V249.1

贾永楠, 田似营, 李擎. 无人机集群研究进展综述[J]. 航空学报, 2020, 41(S1): 723738-723738.

JIA Yongnan, TIAN Siying, LI Qing. Recent development of unmanned aerial vehicle swarms[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S1): 723738-723738.

参考文献 86

[1]	宋怡然,申超,李东兵. 美国分布式低成本无人机集群研究进展[J]. 飞航导弹,2016(8):17-22. SONG Y R, SHEN C, LI D B. A review of the research on distributed, low-cost system for unmanned aerial vehicles (UAVs) swarm of the United States[J]. Aerodynamic Missile Journal, 2016(8):17-22(in Chinese).
[2]	陈晶. 解析美海军低成本无人机蜂群技术[J]. 无人机,2016(1):24-26. CHEN J. The US navy's low-cost swarming drone technology[J]. Unmanned Vehicles, 2016(1):24-26(in Chinese).
[3]	REYNOLDS C W. Flocks, herds, and schools:A distributed behavioral model[J]. ACM SIGGRAPH Computer Graphics, 1987, 21(4):25-34.
[4]	VICSEK T, CZIROK A, JACOB E B, et al. Novel type of phase transitions in a system of self-driven particles[J]. Physical Review Letters, 1995, 75(6):1226.
[5]	VICSEK T. A question of scale[J]. Nature, 2001, 411(6836):421.
[6]	JADBABAIE A, LIN J, MORSE A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules[J]. IEEE Transactions on Automatic Control, 2003, 48(6):988-1001.
[7]	GAZI V, PASSINO K M. Stability analysis of swarms[J]. IEEE Transactions on Automatic Control, 2003, 48(4):692-697.
[8]	OLFATI-SABER R. Flocking for multi-agent dynamic systems:Algorithms and theory[J]. IEEE Transactions on Automatic Control, 2006, 51(3):401-420.
[9]	CUCKER F, SMALE S. Emergent behavior in flocks[J]. IEEE Transactions on Automatic Control, 2007, 52(5):852-862.
[10]	吕娜,刘创,陈柯帆,等. 一种面向航空集群的集中控制式网络部署方法[J]. 航空学报, 2018, 39(7):321961. LYU N, LIU C, CHEN K F, et al. A method for centralized control network deployment of aeronautic swarm[J]. Acta Aerodynamic et Astronautica Sinica, 2018, 39(7):321961(in Chinese).
[11]	JING G, ZHENG Y, WANG L. Group flocking of multiple mobile agents[C]//33rd Chinese Control Conference, 2014:1156-1161.
[12]	CHEN Y, CHANG S. An agent-based simulation for multi-UAVs coordinative sensing[J]. International Journal of Intelligent Computing and Cybernetics, 2008, 1(2):269-284.
[13]	CUCKER F, DONG J. Avoiding collisions in flocks[J]. IEEE Transactions on Automatic Control, 2010, 55(5):1238-1243.
[14]	BAYEZIT I, FIDAN B. Distributed cohesive motion control of flight vehicle formations[J]. IEEE Transactions on Industrial Electronics, 2013, 60(12):5763-5772.
[15]	RAHIMI R, ABDOLLAHI F, NAQSHI K. Time-varying formation control of a collaborative heterogeneous multi agent system[J]. Robotics and Autonomous Systems, 2014, 62(12):1799-1805.
[16]	DONG X, YU B, SHI Z, et al. Time-varying formation control for unmanned aerial vehicles:Theories and applications[J]. IEEE Transactions on Control Systems Technology, 2015, 23(1):340-348.
[17]	周绍磊,祁亚辉,张雷,等. 切换拓扑下无人机集群系统时变编队控制[J]. 航空学报, 2017, 38(4):320452. ZHOU S L, QI Y H, ZHANG L, et al. Time-varying formation control of UAV swarm systems with switching topologies[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(4):320452(in Chinese).
[18]	OYEKAN J. Bio-Inspired vision-based leader-follower formation flying in the presence of delays[J]. Robotics, 2016, 5(3):18.
[19]	KOWNACKI C. Multi-UAV flight using virtual structure combined with behavioral approach[J]. Acta Mechanica et Automatica, 2016,10(2):92-99.
[20]	QIU H, DUAN H. Multiple UAV distributed close formation control based on in-flight leadership hierarchies of pigeon flocks[J]. Aerospace Science and Technology, 2017, 70:471-486.
[21]	ALFEO A L, CIMINO M G C A, DE F N, et al. Design and simulation of the emergent behavior of small drones swarming for distributed target localization[J]. Journal of Computational Science, 2018, 29:19-33.
[22]	JIA Y N, LI Q, ZHANG W C. A distributed cooperative approach for unmanned aerial vehicle flocking[J]. Chaos, 2019, 29(4):043118.
[23]	JIA Y N, YANG Y H, LI Q, et al. Aerial escort task using networked miniature unmanned aerial vehicles[J/OL]. (2019-08-30)[2019-09-11].International Journal of Control, https://www_tandfonline.xilesou.top/doi/abs/10.1080/00207179.2019.1661522.
[24]	BONABEAU E, DORIGO M, THERAULAZ G. Swarm intelligence-from natural to artificial systems[M]. Oxford:Oxford University Press, 1999.
[25]	KUMAR V. The 5S's of aerial robotics:Small, smart, safe, speedy and swarming[C]//CCF-GAIR, 2016.
[26]	HEADQUARTERS. United States air force unmanned aircraft systems flight plan 2016-2036[R]. Washington, D.C.:USAF, 2009.
[27]	CAMBONE S A. Unmanned aircraft systems roadmap 2005-2030[R]. Washington, D.C.:Office of the Secretary of Defense, 2005.
[28]	BALLERINI M, CABIBBO N, CANDELIER R, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance:Evidence from a field study[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(4):1232-1237.
[29]	COUZIN I D, KRAUSE J, FRANKS N R, et al. Effective leadership and decision-making in animal groups on the move[J]. Nature, 2005, 433(7025):513-516.
[30]	MEHES E, VICSEK T. Collective motion of cells:From experiments to models[J]. Integrative Biology, 2014, 6(9):831-854.
[31]	JIANG L, GIUGGIOLI L, PERNA A, et al. Identifying influential neighbors in animal flocking[J]. PloS Computational Biology, 2017, 13(11):e1005822.
[32]	ALFEO A L, CIMINO M G C A, DE F, et al. Swarm coordination of mini-UAVs for target search using imperfect sensors[J]. Intelligent Decision Technologies, 2018, 12(2):149-162.
[33]	ANDREA C, ALESSIO C, IRENE G, et al. Scale-free correlations in starling flocks[J]. Proceedings of the National Academy of Sciences of the National Academy of Sciences of the United States of America, 2010, 107(26):11865-11870.
[34]	CISZAK M, COMPARINI D, MAZZOLAI B, et al. Swarming behavior in plant roots[J]. PloS One, 2012, 7(1):e29759.
[35]	DEUTSCH A, THERAULAZ G, VICSEK T. Collective motion in biological systems[J]. Interface Focus, 2012, 2(6):689-692.
[36]	LI L, XIAO W B, QIU W, et al. New flocking models apply for UAV formation[J]. Journal of Physics:Conference Series, 2019, 1169:012025.
[37]	BENEDETTI M, DURSO F, FORTINO G, et al. A fault-tolerant self-organizing flocking approach for UAV aerial survey[J]. Journal of Network and Computer Applications, 2017, 96:14-30.
[38]	BAHLOUL N E H, BOUDJIT S, ABDENNEBI M, et al. A flocking-based on demand routing protocol for unmanned aerial vehicles[J]. Journal of Computer Science and Technology, 2018, 33(2):263-276.
[39]	DUAN H B, LI P. Autonomous control for unmanned aerial vehicle swarms based on biological collective behaviors[J]. Science & Technology Review, 2017, 35(7):17-25.
[40]	ZHANG T J. Unmanned aerial vehicle formation inspired by bird flocking and foraging behavior[J]. International Journal of Automation and Computing, 2018, 15(4):402-416.
[41]	QUINTERO S, COLLINS G, HESPANHA J. Flocking with fixed-wing UAVs for distributed sensing:A stochastic optimal control approach[C]//Proceedings of the American Control Conference, 2013:2025-2031.
[42]	ZENKEVICH S L, GALUSTYAN N K. Decentralized control of a quadrocopter swarm[J]. Mechatronics, Automation and Control, 2016, 17(11):774-82.
[43]	QIU H X, DUAN H B. Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments[J]. ISA Transactions, 2017, 71(1):93-102.
[44]	JIA Y N, LI Q, QIU S Q. Distributed leader-follower flight control for large-scale clusters of small unmanned aerial vehicles[J]. IEEE Access, 2018, 6:32790-32799.
[45]	MAO X, ZHANG H B, WANG Y H. Flocking of quad-rotor UAVs with fuzzy control[J]. ISA Transactions, 2018, 74:185-193.
[46]	ZHAO W, CHU H, ZHANG M, et al. Flocking control of fixed-wing UAVs with cooperative obstacle avoidance capability[J]. IEEE Access, 2019, 7:17798-17808.
[47]	DAI F, CHEN M, WEI X, et al. Swarm intelligence-inspired autonomous flocking control in UAV networks[J]. IEEE Access, 2019, 7:61786-61796.
[48]	SHEN J. Cucker-smale flocking under hierarchical leadership[J]. Society for Industrial and Applied Mathematics, 2006, 68(3):694-719.
[49]	LI B, LI J, HUANG K W. Modeling and flocking consensus analysis for large-scale UAV swarms[J]. Mathematical Problems in Engineering, 2013, 2013:368369.
[50]	VIRÁGH C, VASARHELYI G, TARCAI N, et al. Flocking algorithm for autonomous flying robots[J]. Bioinspiration and Biomimetics, 2014, 9(2):025012.
[51]	HUNG S, GIVIGI S N. A Q-learning approach to flocking with UAVs in a stochastic environment[J]. IEEE Transactions on Cybernetics, 2017, 47(1):186-197.
[52]	VASARHELYI G, VIRAGH C, SOMORJAI G, et al. Optimized flocking of autonomous drones in confined environments[J]. Science Robotics, 2018, 3(20):eaat3536.
[53]	PARANJAPE A A, CHUNG S, KIM K, et al. Robotic herding of a flock of birds using an unmanned aerial vehicle[J]. IEEE Transactions on Robotics, 2018, 34(4):901-915.
[54]	GARCIA G, KESHMIRI S. Biologically inspired trajectory generation for swarming UAVs using topological distances[J]. Aerospace Science and Technology, 2016, 54:312-319.
[55]	KOWNACKI C, OLDZIEJ D. Fixed-wing UAVs flock control through cohesion and repulsion behaviors combined with a leadership[J/OL]. (2016-01-12)[2017-05-15]. International Journal of Advanced Robotic Systems, https://doi.org/10.5772/62249.
[56]	HE L L, BAI P, LIANG X L, et al. Feedback formation control of UAV swarm with multiple implicit leaders[J]. Aerospace Science and Technology, 2018, 72:327-334.
[57]	CHEN M, DAI F, WANG H, et al. DFM:A distributed flocking model for UAV swarm networks[J]. IEEE Access, 2018, 6:69141-69150.
[58]	BEN-ASHER P G Y, FELDMAN S, FELDMAN M. Distributed decision and control for cooperative UAVs using ad hoc communication[J]. IEEE Transactions on Control System Technology, 2008, 16(3):511-516.
[59]	KHARE V R, WANG F Z, WU S, et al. Ad-hoc network of unmanned aerial vehicle swarms for search and destroy tasks[C]//4th International IEEE Conference on Intelligent Systems. Piscataway:IEEE Press, 2008:665-672.
[60]	HAUERT S, LEVEN S, VARGA M, et al. Reynolds flocking in reality with fixed-wing robots:Communication range vs maximum turning rate[C]//IEEE/RSJ International Conference on Intelligent Robot System, 2011:5015-5020.
[61]	KIM S W, SEO S W. Cooperative unmanned autonomous vehicle control for spatially secure group communications[J]. IEEE Journal on Selected Area in Communications, 2012, 30(5):870-882.
[62]	LUO F, JIANG C, DU J, et al. A distributed gateway selection algorithm for UAV networks[J]. IEEE Transactions on Emerging Topics in Computing, 2015, 3(1):22-33.
[63]	BAYEZIT I, FIDAN B. Distributed cohesive motion control of flight vehicle formations[J]. IEEE Transactions on Industrial Electronics, 2013, 60(12):5763-5772.
[64]	CAO W, XU W. A new multi-UAV cooperation method[C]//9th International Symposium on Computational Intelligence and Design, 2016:231-234.
[65]	JIA Y N. Swarming coordination of multiple unmanned aerial vehicles in three-dimensional space[C]//AIAA Modeling and Simulation Technologies Conference. Reston:AIAA, 2016.
[66]	CIARLETTA L, GUENARD A, PRESSE Y, et al. Simulation and platform tools to develop safe flock of UAVs:A cps application-driven research[C]//International Conference on Unmanned Aircraft Systems, 2014:95-102.
[67]	CORNER J J, LAMONT G B. Parallel simulation of UAV swarm scenarios[C]//Proceedings of the Winter Simulation Conference, 2004:363-371.
[68]	MENDEZ L, GIVIGI S N, SCHWARTZ H M, et al. Validation of swarms of robots:Theory and experimental results[C]//7th International Conference on System of Systems Engineering, 2012:332-337.
[69]	SASKA M. Mav-swarms:Unmanned aerial vehicles stabilized along a given path using onboard relative localization[C]//International Conference on Unmanned Aircraft Systems, 2015:894-903.
[70]	GIL A E, PASSINO K M, GANAPATHY S. Cooperative task scheduling for networked uninhabited air vehicles[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 4(2):561-581.
[71]	JOELIANTO E, SAGALA A. Swarm tracking control for flocking of a multi-agent system[C]//IEEE Conference on Control, Systems and Industrial Informatics, 2012:75-80.
[72]	VÁSÁRHELYI G, VIRÁGH C, SOMORJAI G, et al. Outdoor flocking and formation flight with autonomous aerial robots[C]//IEEE/RSJ International Conference on Intelligent Robot Systems. Piscataway:IEEE Press, 2014:3866-3873.
[73]	BARABASI A-L, ALBERT R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512.
[74]	FANG H, WEI Y, CHEN J, et al. Flocking of second-order multiagent systems with connectivity preservation based on algebraic connectivity estimation[J]. IEEE Transactions on Cybernetics, 2017, 47(4):1067-1077.
[75]	SOORKI M N, TAVAZOEI M S. Adaptive robust control of fractional-order swarm systems in the presence of model uncertainties and external disturbances[J]. IET Control Theory & Applications, 2018, 12(7):961-969.
[76]	SAHU B K, SUBUDHI B. Flocking control of multiple AUVs based on fuzzy potential functions[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5):2539-2551.
[77]	YAZDANI S, HAERI M, SU H. Sampled-data leader-follower algorithm for flocking of multi-agent systems[J]. IET Control Theory and Applications, 2019, 13(5):609-619.
[78]	ZHAN J, LI X. Flocking of multi-agent systems via model predictive control based on position-only measurements[J]. IEEE Transactions on Industrial Informatics, 2013, 9(1):377-385.
[79]	ISKANDARANI M, GIVIGI S N, FUSINA G, et al. Unmanned aerial vehicle formation flying using linear model predictive control[C]//8th Annual IEEE System Conference, 2014:18-23.
[80]	ZHANG H, LIU B, CHENG Z, et al. Model predictive flocking control of the cucker-smale multi-agent model with input constraints[J]. IEEE Transactions on Circuits and Systems I, 2016, 63(8):1265-1275.
[81]	DONG J, QIU L. Flocking of the cucker-smale model on general digraphs[J]. IEEE Transactions on Automatic Control, 2017, 62(10):5234-5239.
[82]	ZHANG H, CHENG Z, CHEN G, et al. Model predictive flocking control for second-order multi-agent systems with input constraints[J]. IEEE Transactions on Circuits and Systems I, 2015, 62(6):1599-1606.
[83]	RAO S, GHOSE D. Sliding mode control-based autopilots for leaderless consensus of unmanned aerial vehicles[J]. IEEE Transactions on Control System Technology, 2014, 22(5):1964-1972.
[84]	JIA Y N, VICSEK T. Modeling hierarchical flocking[J]. New Journal of Physics, 2019, 21:093048.
[85]	JIA Y N, WANG L. Decentralized formation flocking for multiple non-holonomic agents[C]//6th IEEE International Conference on Cybernetics and Intelligent Systems. Piscataway:IEEE Press, 2013:100-105.
[86]	BENNO L, DEMIAN L. The rotating vicsek model:Pattern formation and enhanced flocking in chiral active matter[J]. Physical Review Letters, 2016, 119(5):058002.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

无人机集群研究进展综述

Recent development of unmanned aerial vehicle swarms

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 86

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王海峰, 刘坤澎, 江泓鑫, 杜晨曦. 螺旋桨多设计点气动优化方法和变桨距角策略[J]. 航空学报, 2024, 45(9): 528831-528831.
[2]	赵春晖, 刘安萌, 吕洋, 潘泉. 无人机韧性自主定位技术综述[J]. 航空学报, 2024, 45(8): 28839-028839.
[3]	张薇, 何若俊. 面向物联网数据收集的无人机自主路径规划[J]. 航空学报, 2024, 45(8): 329054-329054-1.
[4]	王传云, 苏阳, 王琳霖, 王田, 王静静, 高骞. 面向反制无人机集群的多目标连续鲁棒跟踪算法[J]. 航空学报, 2024, 45(7): 329017-329017.
[5]	赵靖, 宋丹. 无人机GNSS/IMU组合导航系统完好性监测方法[J]. 航空学报, 2024, 45(7): 328943-328943.
[6]	尹洪玉, 吴宇, 梁天骄. 固定翼无人机巡逻覆盖协同路径规划方法[J]. 航空学报, 2024, 45(6): 328944-328944.
[7]	罗旭东, 吴一全, 陈金林. 无人机航拍影像目标检测与语义分割的深度学习方法研究进展[J]. 航空学报, 2024, 45(6): 28822-028822.
[8]	安朝, 霍贵玺, 孟杨, 谢长川, 杨超. 翼尖铰接组合式无人机气动建模方法及布局参数影响[J]. 航空学报, 2024, 45(6): 629587-629587.
[9]	李广佳, 王红波, 张凯, 仪志胜. 临近空间太阳能无人机增升减阻技术综述[J]. 航空学报, 2024, 45(5): 529644-529644.
[10]	蔡云鹏, 周大鹏, 丁江川. 具有防撞安全约束的无人机集群智能协同控制[J]. 航空学报, 2024, 45(5): 529683-529683.
[11]	王祝, 张梦通, 张振鹏, 徐广通. 基于多指标动态优先级的无人机协同路径规划[J]. 航空学报, 2024, 45(4): 328816-328816.
[12]	郑高杰, 何小明, 李东坡, 谭慧俊, 汪昆, 吴祯龙, 王德鹏. 尾部推进无人机双90°偏折进气道/蜗壳耦合流动特性[J]. 航空学报, 2024, 45(4): 128782-128782.
[13]	杨加秀, 李新凯, 张宏立, 王昊. 切换拓扑下异构集群的强化学习时变编队控制[J]. 航空学报, 2024, 45(10): 329166-329166.
[14]	张清瑞, 刘赟韵, 孙慧杰, 朱波. 固定翼无人机紧密编队的鲁棒协同跟踪控制[J]. 航空学报, 2024, 45(1): 629233-629233.
[15]	郭洪振, 陈谋, 戴永东, 王茂飞. 分布式自适应事件触发四旋翼无人机编队控制[J]. 航空学报, 2023, 44(S2): 729917-729917.