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基于人机合作的无人机实时航迹规划

张帅 李学仁 张鹏 张建业 白晗 赵晓林

张帅, 李学仁, 张鹏, 等 . 基于人机合作的无人机实时航迹规划[J]. 北京航空航天大学学报, 2017, 43(4): 814-822. doi: 10.13700/j.bh.1001-5965.2016.0271
引用本文: 张帅, 李学仁, 张鹏, 等 . 基于人机合作的无人机实时航迹规划[J]. 北京航空航天大学学报, 2017, 43(4): 814-822. doi: 10.13700/j.bh.1001-5965.2016.0271
ZHANG Shuai, LI Xueren, ZHANG Peng, et al. UAV real-time path planning based on human-machine cooperation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 814-822. doi: 10.13700/j.bh.1001-5965.2016.0271(in Chinese)
Citation: ZHANG Shuai, LI Xueren, ZHANG Peng, et al. UAV real-time path planning based on human-machine cooperation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 814-822. doi: 10.13700/j.bh.1001-5965.2016.0271(in Chinese)

基于人机合作的无人机实时航迹规划

doi: 10.13700/j.bh.1001-5965.2016.0271
基金项目: 

国家自然科学基金 61503405

航空科学基金 20145596024

详细信息
    作者简介:

    张帅, 男, 硕士研究生。主要研究方向:无人机实时航迹规划

    李学仁, 男, 教授, 硕士生导师。主要研究方向:无人飞行器协同控制与作战管理

    通讯作者:

    李学仁, E-mail:lixueren@126.com

  • 中图分类号: V279

UAV real-time path planning based on human-machine cooperation

Funds: 

National Natural Science Foundation of China 61503405

Aeronautical Science Foundation of China 20145596024

More Information
  • 摘要:

    针对复杂多变的战场环境,无人机(UAV)在执行任务遇到突发威胁时,提出了一种人机合作的实时航迹规划方法。由人决策和分析威胁信息,给出规避方向和任务紧急程度,无人机据此采用模糊推理的方法,自主解算得到引导点的位置,牵引无人机改变航向,规避突发威胁。仿真结果表明,采用人机合作的实时航迹规划可以将人的智能决策和无人机的快速计算能力有机结合起来,规划更加优化的航迹,能动态调整引导点的位置,可根据任务的紧急程度灵活选择规避路径。

     

  • 图 1  自主航迹规划与人机合作航迹规划方向选择示意图

    Figure 1.  Schematic diagram of direction selection between autonomous path planning and human-machine cooperative path planning

    图 2  航迹节点扩展示意图

    Figure 2.  Schematic diagram of path nodes extension

    图 3  引导点与突发威胁之间的几何关系

    Figure 3.  Geometric relationship between guide point and unexpected threat

    图 4  输入输出隶属函数

    Figure 4.  Membership function of input and output

    图 5  无人机与威胁边界之间角度关系

    Figure 5.  Angle relationship between UAV and unexpected threat's border

    图 6  无人机航迹规划流程

    Figure 6.  UAV path planning procedure

    图 7  无人机自主航迹规划参考航迹

    Figure 7.  UAV autonomous path planning reference path

    图 8  自主航迹规划与人机合作航迹规划

    Figure 8.  UAV autonomous path planning and human-machine cooperative path planning

    图 9  选择不同规避方向时的实时航迹规划

    Figure 9.  Real-time path planning when different evasion directions are chosen

    图 10  不同任务紧急程度时引导点的位置

    Figure 10.  Guide point position with different task emergency levels

    表  1  模糊规则

    Table  1.   Fuzzy rules

    规则序号 输入 输出
    p η θ d
    1 S I L C
    2 S I MH C
    3 S I VH C
    25 VB VI L M
    26 VB VI MH C
    27 VB VI VH C
    下载: 导出CSV

    表  2  威胁具体信息

    Table  2.   Specific information of threats

    威胁序号 中心坐标/km 作用半径/km
    1 (10,25) 6
    2 (16,32) 7
    3 (36,22) 9
    4 (40,42) 6
    5 (47,40) 6
    6 (47,46) 7
    7 (50,10) 7
    下载: 导出CSV

    表  3  自主航迹规划与人机合作航迹规划引导点位置

    Table  3.   Position of guide point with autonomous path planning and human-machine cooperative path planning

    规划方式 d/km 节点数 时间/s 总航程/km 威胁代价 引导点位置/km
    自主航迹规划 1.000 208 0.953 85.996 0.443 (20.300, 14.000)
    人机合作航迹规划 1.403 208 1.137 86.724 0.364 (24.742, 15.697)
    下载: 导出CSV

    表  4  不同规避方向航迹规划结果对比

    Table  4.   Comparison of path planning results with different evasion directions

    规避方向 d/km 节点数 时间/s 总航程/km 威胁代价 引导点位置/km
    左侧引导 1.403 185 1.121 82.404 0.595 (15.258, 24.304)
    右侧引导 1.403 208 1.137 86.724 0.364 (24.742, 15.697)
    下载: 导出CSV

    表  5  不同任务紧急程度航迹规划结果对比

    Table  5.   Comparison of path planning results with different task emergency levels

    任务紧急程度 d/km 节点数 时间/s 总航程/km 威胁代价 引导点位置/km
    η=2 3.595 215 1.144 89.447 0.307 (26.365, 14.224)
    η=5 2.833 212 1.141 88.390 0.328 (25.800, 14.736)
    η=8 1.403 208 1.137 86.724 0.364 (24.742,15.697)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-07
  • 录用日期:  2016-06-20
  • 网络出版日期:  2017-04-20

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