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复杂威胁环境下无人机实时航线规划逻辑架构

刘畅 谢文俊 张鹏 郭庆 肖宗豪 高超

刘畅, 谢文俊, 张鹏, 等 . 复杂威胁环境下无人机实时航线规划逻辑架构[J]. 北京航空航天大学学报, 2020, 46(10): 1948-1957. doi: 10.13700/j.bh.1001-5965.2019.0534
引用本文: 刘畅, 谢文俊, 张鹏, 等 . 复杂威胁环境下无人机实时航线规划逻辑架构[J]. 北京航空航天大学学报, 2020, 46(10): 1948-1957. doi: 10.13700/j.bh.1001-5965.2019.0534
LIU Chang, XIE Wenjun, ZHANG Peng, et al. UAV real-time route planning logical architecture in complex threat environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1948-1957. doi: 10.13700/j.bh.1001-5965.2019.0534(in Chinese)
Citation: LIU Chang, XIE Wenjun, ZHANG Peng, et al. UAV real-time route planning logical architecture in complex threat environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1948-1957. doi: 10.13700/j.bh.1001-5965.2019.0534(in Chinese)

复杂威胁环境下无人机实时航线规划逻辑架构

doi: 10.13700/j.bh.1001-5965.2019.0534
基金项目: 

国家自然科学基金 61703422

装发共用技术项目基金 41412070401

空军工程大学校长基金 XZJY2018026

详细信息
    作者简介:

    刘畅  男, 硕士研究生。主要研究方向:智能规划与辅助决策

    谢文俊  男, 博士, 教授, 硕士生导师。主要研究方向:智能控制与无人作战

    通讯作者:

    谢文俊, E-mail: 870006812@qq.com

  • 中图分类号: V279

UAV real-time route planning logical architecture in complex threat environment

Funds: 

National Natural Science Foundation of China 61703422

Equipment Development Common Technology Project of China 41412070401

Principal Fund of Air Force Engineering University XZJY2018026

More Information
  • 摘要:

    面向高对抗、强拒止的战场环境,实时航线规划是确保无人机(UAV)完成作战任务并提高自身生存概率的重要保障。为使无人机在面临不同程度的复杂威胁环境时能够选择合适的实时航线规划模式,提出了一种基于模糊推理机制的无人机实时航线规划逻辑架构。首先,对实时航线规划模式进行分类,从自主性的角度,重新划分人机权限分配等级,建立了实时航线规划模式与人机权限之间的联系;其次,针对典型观察—判断—决策—行动(OODA)循环存在“信任危机”的风险,构建了一种基于可变自主的实时航线规划体系架构,并对其逻辑进行了说明;最后,利用模糊推理机制实现了无人机系统动态人机权限分配,通过评判人机权限分配等级,进而确定实时航线规划模式。仿真结果表明:验证了实时航线规划逻辑架构的合理性和可变自主方法的有效性;经过综合分析,实时航线规划模式决策结果也比较符合实际作战需求;与模糊综合评价法相比,所提方法降低了人的主观性、实用性更强,得出的结果更加令人信服。

     

  • 图 1  无人机实时航线规划模式

    Figure 1.  UAV real-time route planning mode

    图 2  任务规划系统OODA循环结构

    Figure 2.  OODA loop structure of mission planning system

    图 3  可变自主OOADA循环

    Figure 3.  Variable autonomous OOADA loop

    图 4  基于可变自主的任务规划流程

    Figure 4.  Mission planning process based on variable autonomy

    图 5  无人机系统可变自主体系架构

    Figure 5.  Variable autonomous architecture of UAV system

    图 6  无人机系统自主等级可变的状态迁移

    Figure 6.  UAV autonomous level variable state transition

    图 7  无人机系统自主等级改变原理图

    Figure 7.  Autonomous level change schematic of UAV system

    图 8  人机权限动态分配决策流程

    Figure 8.  Decision-making process of dynamic human-machine authority allocation

    图 9  输入及输出变量的隶属度函数

    Figure 9.  Membership function of input and output variables

    图 10  实时航迹规划模式决策模糊推理框图

    Figure 10.  Block diagram of real-time route planning mode decision-making fuzzy inference

    图 11  OS、TI和CTS与ALUS之间的关系

    Figure 11.  Relationship between OS, TI, CTS and ALUS

    表  1  人机权限分配等级描述

    Table  1.   Description of human-machine authority allocation levels

    等级 名称 无人机权限 操作员权限 实时航线规划模式
    1 完全手动 执行 认知、决策和规划 手动规划
    2 操作员辅助 规划、执行和部分认知 认知主导权和决策权 人机交互实时规划
    3 操作员确认 认知、规划、执行和部分决策 决策主导权 人机协同规划
    4 完全自主 认知、决策、规划和执行 查看反馈数据 自主规划
    下载: 导出CSV

    表  2  输入及输出变量的模糊化处理

    Table  2.   Fuzzy processing of input and output variables

    模糊化处理 语言值 符号 取值范围
    操作员状态模糊化 非常好 VS [0, 0.3]
    比较好 S [0.15, 0.45]
    一般 M [0.35, 0.65]
    比较差 L [0.55, 0.85]
    非常差 VL [0.7, 1]
    任务重要度模糊化 一般 M [0, 1.5]
    比较重要 L [0.8, 2.5]
    非常重要 VL [2, 6]
    战场威胁态势复杂程度模糊化 一般 M [0, 3.5]
    比较复杂 C [1.75, 6.25]
    非常复杂 VC [4.5, 8]
    无人机系统自主等级模糊化 L [1, 4]
    M [3, 7]
    H [6, 10]
    下载: 导出CSV
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  • 收稿日期:  2019-10-09
  • 录用日期:  2019-11-17
  • 网络出版日期:  2020-10-20

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