Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100105-7.doi: 10.11896/jsjkx.240100105

• Network & Communication • Previous Articles     Next Articles

Study on Unmanned Aircraft Formation Control Based on Multi-agent Collaboration

GAN Liangqi, DONG Chao   

  1. College of Electronic and Information Engineering/ College of Integrated Circuits,Nanjing University of Aeronautics and Astro Nautics,Nanjing 211106,China
    Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space,Ministry of Industry and Information Technology,Nanjing 211106,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:GAN Liangqi,born in 1992,Ph.D postgraduate.His main research interests include intelligent network of UAV network and “mosaic warfare”.
    DONG Chao,born in 1980,Ph.D supervisor,is a member of China Computer Federation.His main research interests include intelligent networking and communication of UAV,aviation 6G network,IoT and aerospace bionic science and technology.
  • Supported by:
    National Key Research and Development Project of China(2018YFB1800801) and National Natural Science Foundation of China(61631020,61827801,61931011).

Abstract: The application of unmanned aerial vehicles(UAVs) is receiving more and more attention in all aspects,especially in the complex and changing battlefield,where they have unique advantages.However,single-flight UAV operations in the battlefield are very limited in capability and can only accomplish a single task with low efficiency.Multi-UAV formation coordination can give full play to the advantages of UAV formation and realize wider mission coverage and more efficient mission execution capability.Firstly,the application background and application scenarios of multi-agent networking and UAV formation cooperative dynamic networking are analyzed.The domain of “air-sky-low-altitude-land” joint communication is proposed,which can realize the information between different spatial levels.The joint communication domain of “air-sky-low-altitude-land” is proposed,which can realize the transmission of information and data between different spatial levels,achieve unified command and cooperative operation on the battlefield,and improve the combat efficiency.Then,the mathematical model of a single UAV is established after analysis,and the flight controller of a single UAV is designed and the response times of roll angle,pitch angle and yaw angle are 0.5 s,0.3 s and 2.5 s respectively through simulation.After that,the UAV formation cooperative control system is designed based on tools such as AirSim plug-in,Matlab/Simulink,Python,which can realize the simulation of mission scenarios,the collection and processing of UAV flight data.Finally,in order to solve the problems of data interaction and decision making among UAVs,a cooperative communication control module is designed,and the specific hardware principle and communication protocol for data interaction are given,and the UAV formation cooperative control system is completed through simulation and test.It provides some theoretical support for unmanned combat in modern war and has practical reference significance.

Key words: Multi-UAV formation coordination, Multi-agent, Joint communication domain, Air-Sky-Low-Altitude-Land, Cooperative communication control module

CLC Number: 

  • TN929
[1]军工行业专题报告之无人装备深度研究[EB/OL].2020-10-18[2024-01-09].https://finance.sina.com.cn/stock/stockzmt/2020-10-18/doc-iiznezxr6684066.shtml
[2]中国军网.无人智能作战有哪些优势[EB/OL].2023-01-12[2024-01-09].http://www.81.cn/jfjbmap/content/2023-01/12/content_331710.htm
[3]杨飞.无人机在高原森林灭火中的应用[J].今日消防,2023,8(6):20-23.
[4]GUPTAL,JAINR,VASZKUNG.Survey of Important Issues in UAV Communication Networks[J].IEEE Communications Surveys and Tutorials,2016,18(2):1123-1152.
[5]KINGSTOND,BEARDR,HOLTR.Decentralized Perimeter Surv-eillance Using a Team of UAVs[J].IEEE Transactions on Robotics,2008,24(6):1394-1404.
[6]KAI L,VOICURC,KANHERE SS,et al.Energy efficient legitimate wireless surveillance of UAV communications[J].IEEE Transactions on Vehicular Technology,2019,68(3):2283-2293.
[7]WU Q Q,ZENG Y,ZHANG R.Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks[J].IEEE Transactions on Wireless Communications,2018,17(3):2109-2121.
[8]CHEN J X,XU Y H,WU Q H,et al.Interference-aware Online Distributed Channel Selection for Multicluster FANET:a Potential Game Approach[J].IEEE Transactions on Vehicular Technology,2019,68(4):3792-3804.
[9]ALENAO,NIELSA,JAMESC,et al.Optimization approachesfor civil applications of unmanned aerial vehicles (UAVs) or aerial drones:A survey[J].Networks,2018,72(4):411-458.
[10]GAN L Q.Research on Design and Control Method of Un-manned Aerial Vehicle with Manipulator[D].Guilin:Guilin University of Electronic Technology,2020.
[11]ZONG Q,WANG D D,SHAO S K,et al.Research status and development of multi UAV coordinated formation flight control[J].Journal of Harbin Institute Of Technology,2017,49(3):1-14.
[12]WANG D D.Research on Distributed Formation Keeping andReconfiguration Control for Multiple Unmanned Aerial Vehicles[D].Tianjin:Tianjin University,2019.
[13]REN G S,CHANG J,CHEN W S.Present and Prospect of Intelligent Autonomous Control for UAV[J].Control and Intormation Technology,2018,456(6):13-19.
[14]MENG L H,XU X H,ZHAO Y F.Cooperative coalition for formation flight scheduling based on incomplete information[J].Chinese Journal of Aeronautics,2015,28(6):1747-1757.
[15]LIU Z,GAO X G,FU X W.Coalition Formation for MultipleUAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment[J].Transactions of Nanjing University of Aeronautics and Astronautics,2017(6):688-699.
[16]LI W,CHEN J.Review and Prospect of Cooperative Combat of Manned/Unmanned Aerial Vehicle Hybrid Formation[J].Aerospace Control,2017,35(3):90-96.
[17]REYNOLDS C W.Flocks,herds and schools:A distributed behavioral model[J].ACM SIGGRAPH Computer Graphics,1987,21(4):25.
[18]QIU H X,DUAN H B.From collective flight in bird flocks to unmanned aerial vehicle autonomous swarm formation[J].Chinese Journal of Engineering,2017,39(3):317-322.
[19]河北新闻网.河北涿州:无人机架起“空中基站”[EB/OL].2023[2024-01-09].https://hebei.hebnews.cn/2023-08/04/content_9050000.htm.
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