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基于合作博弈的多机冲突解脱算法

张宏宏 甘旭升 辛建霖 刘一群 陈旭祎

张宏宏, 甘旭升, 辛建霖, 等 . 基于合作博弈的多机冲突解脱算法[J]. 北京航空航天大学学报, 2022, 48(5): 863-871. doi: 10.13700/j.bh.1001-5965.2020.0670
引用本文: 张宏宏, 甘旭升, 辛建霖, 等 . 基于合作博弈的多机冲突解脱算法[J]. 北京航空航天大学学报, 2022, 48(5): 863-871. doi: 10.13700/j.bh.1001-5965.2020.0670
ZHANG Honghong, GAN Xusheng, XIN Jianlin, et al. Multi-aircraft conflict resolution algorithm based on cooperative game[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 863-871. doi: 10.13700/j.bh.1001-5965.2020.0670(in Chinese)
Citation: ZHANG Honghong, GAN Xusheng, XIN Jianlin, et al. Multi-aircraft conflict resolution algorithm based on cooperative game[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 863-871. doi: 10.13700/j.bh.1001-5965.2020.0670(in Chinese)

基于合作博弈的多机冲突解脱算法

doi: 10.13700/j.bh.1001-5965.2020.0670
基金项目: 

国家自然科学基金 61601497

空军工程大学校长基金 XZJ2020005

详细信息
    通讯作者:

    甘旭升, E-mail: gxsh15934896556@qq.com

  • 中图分类号: V279

Multi-aircraft conflict resolution algorithm based on cooperative game

Funds: 

National Natural Science Foundation of China 61601497

President's Foundation of Air Force Engineering University XZJ2020005

More Information
  • 摘要:

    为解决低空无人机冲突解脱过程中个体支付成本不公平问题,提出了基于合作博弈“核仁解”概念的多机冲突解脱算法。针对低空多机冲突场景的特点,基于“核仁解”概念,建立无人机冲突解脱支付矩阵。结合人工势场法与蚁群算法的优点,提出基于人工势场法-蚁群算法(APF-ACO)的冲突解脱混合求解策略。仿真结果表明:综合计算时间、可行性与系统效率3个评价指标,APF-ACO混合求解策略效能最优;基于合作博弈“核仁解”的求解策略在一定程度上可提升个体公平性;同时能够在牺牲少量整体利益的前提下,拥有优先级无人机的快速规划达到目标。

     

  • 图 1  人工势场法示意图

    Figure 1.  Schematic diagram of artificial potential field method

    图 2  单步机动策略编码示意图

    Figure 2.  Coding schematic diagram of one-step maneuver strategy

    图 3  无人机冲突解脱策略编码示意

    Figure 3.  UAV conflict resolution strategy coding

    图 4  基于APF-ACO算法的多机冲突解脱流程

    Figure 4.  Multi-aircraft conflict resolution process based on APF-ACO algorithm

    图 5  经典8机对头冲突场景

    Figure 5.  Classic eight aircraft-to-head flight conflict scenario

    图 6  无人机机流冲突场景

    Figure 6.  Conflict scenario of UAV flow

    图 7  8机对头冲突解脱

    Figure 7.  Eight aircraft-to-head flight conflict resolution

    图 8  冲突解脱求解策略性能对比

    Figure 8.  Performance comparison of conflict resolution solution strategies

    图 9  两种冲突解脱策略下个体公平度对比

    Figure 9.  Comparison of individual fairness between two conflict resolution strategies

    图 10  无人机优先级调配策略

    Figure 10.  Priority allocation strategy of UAV

    表  1  仿真参数设置

    Table  1.   Simulation parameter setting

    参数 数值 参数 数值
    d/m 20 α 100
    kri 1 m 60
    kvi 1 ρ 0.8
    ki/(m·s-1) 10 Q 500
    rj/m 3 NCmax 50
    σrj/m 50 L 20
    下载: 导出CSV

    表  2  两种冲突解脱策略性能对比

    Table  2.   Performance comparison of two conflict resolution strategies

    求解策略 个体支付成本υ/m
    UAV1 UAV2 UAV3 UAV4 UAV5 UAV6
    合作博弈“核仁解” 6.119 2 62.542 3 23.304 0 62.374 0 62.092 8 7.259 3
    传统合作式 117.294 3 32.746 2 21.049 4 21.049 4 23.304 0 15.624 0
    下载: 导出CSV

    表  3  两种无人机优先级调配策略下的个体支付成本

    Table  3.   Individual payment costs of two UAV priority allocation strategies

    UAV 个体支付成本υ/m
    不考虑优先级的调配策略 基于合作博弈“核仁解”的优先调配
    UAV1 19.467 3 8.038 5
    UAV2 19.516 7 53.687 1
    UAV3 20.890 5 15.713 9
    UAV4 20.890 5 15.652 5
    总计 80.765 93.092
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-01
  • 录用日期:  2021-01-08
  • 网络出版日期:  2022-05-20

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