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基于修正导航向量场的AUV自主避障方法

姚鹏 解则晓

姚鹏, 解则晓. 基于修正导航向量场的AUV自主避障方法. 自动化学报, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
引用本文: 姚鹏, 解则晓. 基于修正导航向量场的AUV自主避障方法. 自动化学报, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
Yao Peng, Xie Ze-Xiao. Autonomous obstacle avoidance for AUV based on modified guidance vector field. Acta Automatica Sinica, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
Citation: Yao Peng, Xie Ze-Xiao. Autonomous obstacle avoidance for AUV based on modified guidance vector field. Acta Automatica Sinica, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219

基于修正导航向量场的AUV自主避障方法

doi: 10.16383/j.aas.c180219
基金项目: 

山东省自然科学基金 ZR2018BF016

中国博士后科学基金 2017M622278

中央高校基本科研业务费 201713046

详细信息
    作者简介:

    解则晓  中国海洋大学工程学院教授.主要研究方向为机器视觉与水下三维测量. E-mail: xiezexiao@ouc.edu.cn

    通讯作者:

    姚鹏  中国海洋大学工程学院讲师.主要研究方向为无人系统路径规划与智能决策, 多机器人协同优化与控制.本文通信作者.E-mail: yaopenghappy@163.com

Autonomous Obstacle Avoidance for AUV Based on Modified Guidance Vector Field

Funds: 

Natural Science Foundation of China ZR2018BF016

China Postdoctoral Science Foundation 2017M622278

Fundamental Research Funds for the Central Universities 201713046

More Information
    Author Bio:

    XIE Ze-Xiao Professor at the College of Engineering, Ocean University of China. His research interest covers machine vision and underwater 3D measurement technology

    Corresponding author: YAO Peng Lecturer at the College of Engineering, Ocean University of China. His research interest covers path planning and intelligent decision of unmanned system, cooperative optimization and control of multi-robots. Corresponding author of this paper
  • 摘要: 针对复杂海洋环境下的自治水下机器人(Autonomous underwater vehicle, AUV)三维避障问题, 本文提出了一种高效的修正导航向量场方法.构建自由空间下的初始导航向量场, 引导AUV以最短路径向目标点航行.定义修正矩阵来量化描述障碍物对初始导航向量场的影响, 得到障碍空间下的修正导航向量场, 使得AUV向目标点航行的同时躲避静态障碍.通过结合障碍物运动速度, 分别构建相对初始导航向量场与相对修正导航向量场, 并采取有限时域推演与调整策略, 最终引导AUV安全躲避动态障碍.仿真结果表明, 本方法能较好地应用于复杂海洋环境下的AUV避障任务.
  • 图  1  速度矢量关系图

    Fig.  1  Relationship between velocity vectors

    图  2  海洋环境下的典型凸面体障碍物

    Fig.  2  Convex obstacles in ocean environment

    图  3  AUV自主避障示意图

    Fig.  3  Illustration of AUV avoiding obstacles

    图  4  导航向量场示意图

    Fig.  4  Illustration of guidance vector fields

    图  5  AUV躲避圆球障碍物

    Fig.  5  AUV avoiding a sphere obstacle

    图  6  AUV躲避凹陷区域

    Fig.  6  AUV avoiding concave area

    图  7  AUV躲避动态障碍物

    Fig.  7  AUV avoiding dynamic obstacles

    图  8  AUV部分状态量与控制输入

    Fig.  8  AUV state value and control input

    图  9  复杂场景下AUV自主避障

    Fig.  9  AUV avoiding obstacles in a complex scenario

    表  1  APF与MGVF方法的量化指标对比

    Table  1  Performance indicators of APF and MGVF

    方法 $L {\rm{(m)}}$ $GS (^\circ)$ $LS (^\circ)$ $d_{ \text{{AUV- obs}}}^{\min } {\rm{(m)}}$
    APF 488 2.08 14.11 0.3
    MGVF 453 1.14 5.56 5.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-16
  • 录用日期:  2018-09-21
  • 刊出日期:  2020-08-26

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