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基于空海异构无人平台的水下目标搜索与跟踪

丁文俊 柴亚军 杨宇贤 刘佳敏 毛昭勇

丁文俊, 柴亚军, 杨宇贤, 等. 基于空海异构无人平台的水下目标搜索与跟踪[J]. 水下无人系统学报, 2024, 32(2): 237-249 doi: 10.11993/j.issn.2096-3920.2024-0037
引用本文: 丁文俊, 柴亚军, 杨宇贤, 等. 基于空海异构无人平台的水下目标搜索与跟踪[J]. 水下无人系统学报, 2024, 32(2): 237-249 doi: 10.11993/j.issn.2096-3920.2024-0037
DING Wenjun, CHAI Yajun, YANG Yuxian, LIU Jiamin, MAO Zhaoyong. Underwater Target Search and Tracking Based on Air-Sea Heterogeneous Unmanned Platform[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 237-249. doi: 10.11993/j.issn.2096-3920.2024-0037
Citation: DING Wenjun, CHAI Yajun, YANG Yuxian, LIU Jiamin, MAO Zhaoyong. Underwater Target Search and Tracking Based on Air-Sea Heterogeneous Unmanned Platform[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 237-249. doi: 10.11993/j.issn.2096-3920.2024-0037

基于空海异构无人平台的水下目标搜索与跟踪

doi: 10.11993/j.issn.2096-3920.2024-0037
基金项目: 国家自然科学基金项目(51909206); 中国博士后科学基金项目(2021M692616); 陕西省自然科学基础研究计划项目(2024JC-YBMS-300); 中央高校基本科研业务费专项资金项目(31020200QD044)
详细信息
    作者简介:

    丁文俊(1989-), 男, 博士, 副教授, 主要研究方向为水下无人系统、跨域协同及智能决策

  • 中图分类号: TJ630.32; U674

Underwater Target Search and Tracking Based on Air-Sea Heterogeneous Unmanned Platform

  • 摘要: 海上异构无人系统可有效提高复杂任务的完成效率。文中采用自主水下航行器(AUV)和无人机(UAV)来完成近海海域内未知水下目标的搜索与跟踪任务。首先, 描述了水下目标搜索跟踪任务, 将任务过程分为目标搜索和目标跟踪阶段, 2个阶段的目标分别是使AUV&UAV总搜索空间最大化以及AUV与水下目标的末端位置误差最小; 然后, 建立AUV&UAV跨域协同搜索模型, 并设定模型中AUV和UAV探测范围和通信距离等约束条件; 最后, 在跨域协同搜索与路径跟踪规划中, 基于传统粒子群算法, 加入自适应学习因子调控策略和精英保存策略, 生成搜索与跟踪路径。仿真实验表明, 采用改进粒子群优化算法的AUV&UAV异构无人系统能够更高效地完成水下目标搜索与跟踪任务。

     

  • 图  1  自由地图模型示意图

    Figure  1.  Diagram of free map model

    图  2  UAV与AUV模型示意图

    Figure  2.  Model of UAV and AUV

    图  3  IPSO算法流程图

    Figure  3.  IPSO algorithm flow chart

    图  4  基于PSO算法的任务全过程二维路径曲线

    Figure  4.  The two-dimensional path map of the whole process of a task based on PSO algorithm

    图  5  基于PSO算法的任务全过程三维路径图

    Figure  5.  3D path map of the whole process of the task based on PSO algorithm

    图  6  基于PSO算法的全过程无人平台间距变化曲线

    Figure  6.  The whole process of unmanned platform spacing variation curve based on PSO algorithm

    图  7  基于IPSO算法的任务全过程的二维路径图

    Figure  7.  The two-dimensional path map of the whole process of a task based on IPSO algorithm

    图  8  基于IPSO算法的任务全过程三维路径图

    Figure  8.  3D path map of the whole process of the task based on IPSO algorithm

    图  9  基于IPSO算法的全过程无人平台间距变化曲线

    Figure  9.  The whole process of unmanned platform spacing variation curves based on IPSO algorithm

    表  1  UAV、AUV及二维定深运动目标的初始状态信息

    Table  1.   Initial state information of UAV, AUV and two-dimensional depth-fixed moving target

    运动物体坐标/m速度
    /(m/s)
    姿态角/rad
    xyz$\varphi $$\theta $
    UAV20005015π/6
    AUV050−50800
    目标1 0001 000−50500
    下载: 导出CSV

    表  2  基于PSO算法任务的相关参数

    Table  2.   Other data of task based on PSO algorithm

    参数数值
    TU/s41.00
    TA/s241.00
    TA-T/s400.00
    LA-T/m1.86
    下载: 导出CSV

    表  3  基于IPSO算法任务的相关参数

    Table  3.   Other data of UAV & AUV heterogeneous system task based on IPSO algorithm

    参数数值
    TU/s35.00
    TA/s180.00
    TA-T/s344.00
    LA-T/m0.69
    下载: 导出CSV

    表  4  PSO算法和IPSO算法仿真结果

    Table  4.   Some experimental results of PSO algorithm

    参数PSO仿真结果平均值
    TU/s414037543240.8
    TA/s241227244261217238
    TA-T/s400366384416341381.4
    LA-T/m1.862.682.231.561.722.01
    参数IPSO仿真结果平均值
    TU/s353025304234.4
    TA/s180217203175210197
    TA-T/s344347253230361307
    LA-T/m1.691.830.831.531.621.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-03-10
  • 修回日期:  2024-04-01
  • 网络出版日期:  2024-05-06

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