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基于“雁阵效应” 的扑翼飞行机器人高效集群编队研究

尹曌 贺威 邹尧 穆新星 孙长银

尹曌, 贺威, 邹尧, 穆新星, 孙长银. 基于“雁阵效应” 的扑翼飞行机器人高效集群编队研究. 自动化学报, 2021, 47(6): 1355−1367 doi: 10.16383/j.aas.c190900
引用本文: 尹曌, 贺威, 邹尧, 穆新星, 孙长银. 基于“雁阵效应” 的扑翼飞行机器人高效集群编队研究. 自动化学报, 2021, 47(6): 1355−1367 doi: 10.16383/j.aas.c190900
Yin Zhao, He Wei, Zou Yao, Mu Xin-Xing, Sun Chang-Yin. Efficient formation of flapping-wing aerial vehicles based on wild geese queue effect. Acta Automatica Sinica, 2021, 47(6): 1355−1367 doi: 10.16383/j.aas.c190900
Citation: Yin Zhao, He Wei, Zou Yao, Mu Xin-Xing, Sun Chang-Yin. Efficient formation of flapping-wing aerial vehicles based on wild geese queue effect. Acta Automatica Sinica, 2021, 47(6): 1355−1367 doi: 10.16383/j.aas.c190900

基于“雁阵效应” 的扑翼飞行机器人高效集群编队研究

doi: 10.16383/j.aas.c190900
基金项目: 国家自然科学基金(61933001, 61921004), 北京科技大学中央高校基本科研业务费专项资金(FRF-TP-19-001C2), 北京高校高精尖学科“北京科技大学 — 人工智能科学与工程” 资助
详细信息
    作者简介:

    尹曌:北京科技大学自动化学院控制科学与工程专业博士研究生. 2016年获得电子科技大学自动化工程学院控制工程专业硕士学位. 主要研究方向为扑翼飞行机器人控制, 自适应控制, 多智能体控制. E-mail: yinzhao0312@163.com

    贺威:北京科技大学自动化学院教授. 2006年获得华南理工大学自动化学院学士学位, 2011年获得新加坡国立大学电气工程与计算机科学系博士学位. 主要研究方向为机器人学, 分布参数系统控制, 扑翼飞行机器人控制, 振动控制和智能控制系统. 本文通信作者. E-mail: weihe@ieee.org

    邹尧:北京科技大学自动化学院副教授. 2010年获得大连理工大学自动化学院学士学位, 2016年获得北京航空航天大学控制科学与工程博士学位. 主要研究方向为非线性控制, 无人机控制, 多智能体控制. E-mail: zouyao@ustb.edu.cn

    穆新星:北京科技大学自动化学院控制科学与工程专业博士研究生. 主要研究方向为扑翼飞行机器人控制, 智能控制, 系统建模. E-mail: muxinxing@sina.cn

    孙长银:东南大学自动化学院教授. 1996年获得四川大学应用数学专业理学学士学位. 分别于2001年, 2004年获得东南大学硕士和博士学位. 主要研究方向为智能控制, 飞行器控制, 模式识别和优化理论. E-mail: cysun@seu.edu.cn

Efficient Formation of Flapping-wing Aerial Vehicles Based on Wild Geese Queue Effect

Funds: Supported by National Natural Science Foundation of China (61933001, 61921004), Fundamental Research Funds for the China Central Universities of University of Science and Technology Beijing (FRF-TP-19-001C2), and Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing
More Information
    Author Bio:

    YIN Zhao Ph. D. candidate in Control Science and Engineering with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his master degree in Control Engineering from the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China, in 2016. His research interest covers control of flapping wing aerial vehicle, adaptive control, and multi-agent control

    HE Wei Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his bachelor degree from College of Automation Science and Engineering, South China University of Technology (SCUT), China in 2006, and his Ph. D. degree from Department of Electrical & Computer Engineering, National University of Singapore (NUS), Singapore in 2011. His research interest covers robotics, control of distributed parameter systems, control of flapping-wing air vehicles, vibration control and intelligent control systems. Corresponding author of this paper

    ZOU Yao Associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his bachelor degree in Automation from Dalian University of Technology (DUT), in 2010, and Ph. D. degree in control science and engineering from Beihang University (BUAA, formerly named Beijing University of Aeronautics and Astronautics), in 2016. His research interest covers nonlinear control, unmanned aerial vehicle control, and multi-agent control

    MU Xin-Xing Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers control of flapping wing aerial vehicle, intelligent control, and system modeling

    SUN Chang-Yin Professor at the School of Automation, Southeast University. He received his bachelor degree from College of Mathematics, Sichuan University in 1996, and his master and Ph. D. degrees in electrical engineering from the Southeast University respectively, in 2001 and 2004. His research interest covers intelligent control, flight control, pattern recognition, and optimal theory

  • 摘要: 本文借鉴“雁阵效应”, 研究了扑翼飞行机器人高效集群编队飞行问题. 通过对“V”字雁阵的分析得知, 当前排大雁(简称头雁)和后排大雁(简称从雁)保持某一合适的相对位置偏移时, 后排大雁可有效利用前排大雁挥翅产生的上洗涡流, 从而节省体能; 并且, 雁阵通过阵型的变换, 可以实现能量整体消耗的均衡性, 确保长航时飞行. 仿照该“雁阵效应”, 分析得出耗能最少的扑翼飞行机器人集群阵型排布方式, 并设计了阵型变换机制, 实现集群能量整体消耗的最优性和均衡性. 在此基础上, 参考雁群的交互方式, 设计了一种使用局部信息的控制方法, 保证最优阵型的稳定维持以及阵型间的灵活变换. 最后, 仿真结果验证了所提理论结果的有效性.
  • 图  1  锐角“V” 字阵型示意图($\alpha < 90^{\circ}$)

    Fig.  1  “V” configuration with an acute angle ($\alpha < 90^{\circ}$)

    图  2  钝角“V” 字阵型示意图($\alpha > 90^{\circ}$)

    Fig.  2  “V” configuration with an obtuse angle ($\alpha > 90^{\circ}$)

    图  3  雁间距及翼尖涡流示意图

    Fig.  3  The schematic diagrams of spacing between wild goose and vortex formed by wingtip

    图  4  扑翼飞行机器人飞行涡流模型俯视图

    Fig.  4  Top view of two flapping-wing aerial vehicles

    图  5  扑翼飞行机器人飞行涡流模型后视图

    Fig.  5  View from behind of two flapping-wing aerial vehicles

    图  6  扑翼飞行机器人“僚机” 机翼升力偏转侧视图

    Fig.  6  Sideview of follower' s wing lift rotation

    图  7  两机纵向间距为2b时升力变化关于横向间距、垂向间距3维曲线图

    Fig.  7  3D curve of lift variation with respect to lateral and vertical distances with longitudinal distance 2b

    图  8  两机纵向间距为2b时阻力变化关于横向间距、垂向间距3维曲线图

    Fig.  8  3D curve of drag variation with respect to lateral and vertical distances with longitudinal distance 2b

    图  9  扑翼飞行机器人集群编队阵型

    Fig.  9  Configuration of flapping-wing aerial vehicles

    图  10  扑翼飞行机器人集群阵型变换

    Fig.  10  Reconfiguration of flapping-wing aerial vehicles

    图  11  扑翼飞行机器人集群编队控制框图

    Fig.  11  Formation control block diagram of flapping-wing aerial vehicles

    图  12  扑翼飞行机器人集群编队飞行及阵型变换三维图

    Fig.  12  3D formation snapshot of flapping-wing aerial vehicles

    图  13  1号和2号扑翼飞行机器人相对位置分量

    Fig.  13  Relative position components between flapping-wing aerial vehicles 1 and 2

    图  14  1号和3号扑翼飞行机器人相对位置分量

    Fig.  14  Relative position components between flapping-wing aerial vehicles 1 and 3

    图  15  2号和4号扑翼飞行机器人相对位置分量

    Fig.  15  Relative position components between flapping-wing aerial vehicles 2 and 4

    图  16  3号和5号扑翼飞行机器人相对位置分量

    Fig.  16  Relative position components between flapping-wing aerial vehicles 3 and 5

    图  17  扑翼飞行机器人飞行速度曲线

    Fig.  17  Velocities of flapping-wing aerial vehicles

    图  18  扑翼飞行机器人飞行航向角曲线

    Fig.  18  Yaws of flapping-wing aerial vehicles

    图  19  扑翼飞行机器人飞行功率消耗曲线

    Fig.  19  Power of flapping-wing aerial vehicles

    图  20  2号扑翼飞行机器人基于不同的编队阵型下的飞行功率消耗曲线

    Fig.  20  Power of flapping-wing aerial vehicles 2 in different formations

    图  21  扑翼飞行机器人集群编队飞行三维虚拟仿真实验图

    Fig.  21  3D virtual simulation snapshot for formation of flapping-wing aerial vehicles

    表  1  扑翼飞行机器人基本参数

    Table  1  Parameters of flapping-wing aerial vehicles

    名称 符号参数单位
    机翼翼面面积S0.175m2
    翼展b0.8m
    展弦比AR3.66
    飞机质量m0.1kg
    机翼升力曲线斜率aW8.2rad−1
    升力系数CL1.0
    动压q16.125kg/m2
    下载: 导出CSV

    表  2  扑翼飞行机器人仿真参数

    Table  2  Simulation parameters of flapping-wing aerial vehicles

    扑翼机编号x (m)y (m)z (m)v (m/s)γ (°)
    扑翼机 1 号 0 0 2 1 0
    扑翼机 2 号 −0.8 −2 2 3 10
    扑翼机 3 号 0.8 −4 2 3 −10
    扑翼机 4 号 −1.6 −3 2 3 20
    扑翼机 5 号 1.6 −4 2 2 −20
    下载: 导出CSV
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  • 收稿日期:  2019-12-31
  • 网络出版日期:  2021-06-10
  • 刊出日期:  2021-06-10

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