针对动态目标的多无人机协同组合差分进化搜索方法
作者:
作者单位:

1.厦门大学航空航天学院;2.西北工业大学民航学院

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中图分类号:

TP273

基金项目:

国家自然科学基金(批准号: 61673327); 2021年度太仓市基础研究计划项目(TC2021JC28); 中央高校基本科研业务费资助项目(G2021KY05116); 2021年西北工业大学太仓长三角研究院产业发展引导培育项目(CY20210202)


A composite differential evolution algorithm for multi-UAV cooperative dynamic target search
Author:
Affiliation:

1.School of Areospace Engineering,Xiamen University;2.School of Civil Aviation, Northwestern Polytechnic University

Fund Project:

National Natural Science Foundation of China (61673327);2021 Taicang Fundamental Research Plan Project (TC2021JC28);Fundamental Research Funds for the Central Universities (G2021KY05116);Industrial Development and Foster Project of Yangtze River Delta Research Institute of NPU, Taicang (CY20210202)

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    摘要:

    针对多无人机动态目标协同搜索问题, 提出了一种组合差分进化无人机协同搜索航迹规划方法. 首先, 建立了动态目标协同搜索环境信息图模型及无人机运动模型. 接着, 基于改进差分蝙蝠算法和自适应差分进化算法, 设计了基于种群数量自适应分配的组合框架, 将差分进化算法中的变异、交叉和选择机制引入蝙蝠算法, 构建了组合差分进化算法的协同搜索算法, 并对无人机动态目标协同搜索的航迹进行了优化. 针对待搜索目标轨迹随机多变且具有规避侦察特性的现实场景, 建立了可回访数字信息图和自适应目标搜索增益函数, 提高了无人机对动态目标的捕获能力. 最后, 仿真结果表明所提出的无人机动态目标协同搜索算法的有效性.

    Abstract:

    To solve the problem of multi-UAV cooperative search for dynamic target, a composite differential evolution algorithm is proposed for multiple UAVs to perform cooperative dynamic target search. First, the environment information graph model for the cooperative dynamic target search and UAV dynamic model are established. Then, based on improved differential bat algorithm and adaptive differential evolution algorithm, a combinatorial framework relied on population adaptive allocation is designed. By introducing the mutation, crossover and selection mechanisms of differential evolution algorithm to the bat algorithm, a cooperative search algorithm combined with differential evolution algorithm is constructed. For the real scenario where the trajectories of dynamic targets are random and unpredictable and have reconnaissance evasion trait, retrievable digital information graph and adaptive target search gain function are established to enhance the capabilities of UAVs to capture dynamic targets. Finally, simulation results demonstrate the effectiveness of the proposed UAV cooperative dynamic target search algorithm.

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  • 收稿日期:2021-12-28
  • 最后修改日期:2023-01-13
  • 录用日期:2022-06-10
  • 在线发布日期: 2022-06-28
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