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基于可达集的无人机低空飞行冲突解脱算法

杨建航 张福彪 王江

杨建航,张福彪,王江. 基于可达集的无人机低空飞行冲突解脱算法[J]. 北京航空航天大学学报,2023,49(7):1813-1827 doi: 10.13700/j.bh.1001-5965.2021.0542
引用本文: 杨建航,张福彪,王江. 基于可达集的无人机低空飞行冲突解脱算法[J]. 北京航空航天大学学报,2023,49(7):1813-1827 doi: 10.13700/j.bh.1001-5965.2021.0542
YANG J H,ZHANG F B,WANG J. Conflict resolution algorithms for UAV low-altitude flight based on reachable set[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1813-1827 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0542
Citation: YANG J H,ZHANG F B,WANG J. Conflict resolution algorithms for UAV low-altitude flight based on reachable set[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1813-1827 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0542

基于可达集的无人机低空飞行冲突解脱算法

doi: 10.13700/j.bh.1001-5965.2021.0542
基金项目: 国家重点研发计划(2021YFF0601304);国家自然科学基金(U1913602); 北京市科学技术委员会基金(Z181100003218013)
详细信息
    通讯作者:

    E-mail:fubiao.zhang@bit.edu.cn

  • 中图分类号: V221+.3;TB553

Conflict resolution algorithms for UAV low-altitude flight based on reachable set

Funds: National Key Research and Development Program of China (2021YFF0601304); National Natural Science Foundation of China (U1913602); Beijing Municipal Science & Technology Commission fund (Z181100003218013)
More Information
  • 摘要:

    针对无人空中交通管理(UTM)中的冲突解脱问题,提出了以可达集分析为基础的实时避撞算法。该算法可用于城市低空环境中的密集交通流空域,保证无人机(UAV)飞行过程的安全性。基于相对运动的概念,通过分析平面空域中的飞行博弈问题对避撞系统进行建模,同时利用水平集方法和最优控制理论对无人机的可达集进行分析和计算,使用机载传感器获取无人机与周围物体的信息,为每架无人机提供新的避撞策略。通过3种不同空域环境的飞行案例进行仿真实验,验证了该策略不仅可以得到平滑的飞行路径,实时安全地解决冲突解脱问题,而且针对合作/非合作目标均有效。

     

  • 图 1  前向可达集与后向可达集[30]

    Figure 1.  Forward reachable set and backward reachable set[30]

    图 2  无人机前后向可达集确保安全方式

    Figure 2.  FRS and BRS ensure safety measures

    图 3  水平集方法的图示[32]

    Figure 3.  Illustration of level set method[32]

    图 4  2架飞机相对运动系统模型

    Figure 4.  Two aircraft relative motion system model

    图 5  目标集与后向可达集

    Figure 5.  Target set and backward reachable set

    图 6  可达集随时间变化

    Figure 6.  Reachable set changes over time

    图 7  避撞动画的注释帧

    Figure 7.  Comment frame of collision avoidance animation

    图 8  飞机Ⅱ避撞示意图

    Figure 8.  Aircraft Ⅱ cannot enter the reachable set, thus avoiding collision

    图 9  无人机与障碍物模型

    Figure 9.  UAV and obstacle model

    图 10  可达集与3种区域的关系

    Figure 10.  Relation between reachable set and three areas

    图 11  UAV避障模型

    Figure 11.  UAV collision avoidance model

    图 12  本文算法工作流程

    Figure 12.  Work flow of proposed algorithm

    图 13  用于仿真的3种案例

    Figure 13.  Three cases for simulation

    图 14  案例1的可达集

    Figure 14.  Reachable set of case 1

    图 15  无人机躲避多个静态障碍物的过程

    Figure 15.  Process of UAV avoiding multiple static obstacles

    图 16  无人机与静态障碍物之间的距离

    Figure 16.  Distance between UAV and static obstacles

    图 17  无人机速度和飞行平面内推力(案例1)

    Figure 17.  UAV speed and thrust in flight plane (case 1)

    图 18  案例2的可达集

    Figure 18.  Reachable set of case 2

    图 19  2架无人机相互避撞的过程

    Figure 19.  Process of two UAVs avoiding each other

    图 20  2架无人机之间的距离

    Figure 20.  Distance between two UAVs

    图 21  无人机1速度和飞行平面内推力(案例2)

    Figure 21.  UAV 1 speed and thrust in flight plane (case 2)

    图 22  无人机2速度和飞行平面内推力(案例2)

    Figure 22.  UAV 2 speed and thrust in flight plane (case 2)

    图 23  案例3的可达集

    Figure 23.  Reachable set of case 3

    图 24  无人机躲避多个动态障碍物的过程

    Figure 24.  Process of UAV avoiding multiple dynamic objects

    图 25  无人机速度和飞行平面内推力(案例3)

    Figure 25.  UAV speed and thrust in flight plane (case 3)

    图 26  无人机与动态物体之间的距离

    Figure 26.  Distance between UAV and dynamic objects

    表  1  仿真参数

    Table  1.   Simulation parameters

    参数数值
    无人机初始位置${ {\boldsymbol{p} }_{\text{0} } }/{\rm{m}}$[0 , 0]
    反应时间/s1
    无人机质量$ m $/kg20
    无人机半径$ r $/m1.5
    无人机的初始速度${\boldsymbol{v} }/({\rm{m}} \cdot {{\rm{s}}}^{-1})$[40 , 0]
    冲突探测半径$ R $/m120
    障碍物半径${r_{{\rm{obs}}} }$/m15
    可达集获取时间/s0.097
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-09
  • 录用日期:  2021-12-17
  • 网络出版日期:  2022-01-12
  • 整期出版日期:  2023-07-31

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