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基于水下机器人的海产品智能检测与自主抓取系统

徐凤强 董鹏 王辉兵 付先平

徐凤强, 董鹏, 王辉兵, 等 . 基于水下机器人的海产品智能检测与自主抓取系统[J]. 北京航空航天大学学报, 2019, 45(12): 2393-2402. doi: 10.13700/j.bh.1001-5965.2019.0377
引用本文: 徐凤强, 董鹏, 王辉兵, 等 . 基于水下机器人的海产品智能检测与自主抓取系统[J]. 北京航空航天大学学报, 2019, 45(12): 2393-2402. doi: 10.13700/j.bh.1001-5965.2019.0377
XU Fengqiang, DONG Peng, WANG Huibing, et al. Intelligent detection and autonomous capture system of seafood based on underwater robot[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2393-2402. doi: 10.13700/j.bh.1001-5965.2019.0377(in Chinese)
Citation: XU Fengqiang, DONG Peng, WANG Huibing, et al. Intelligent detection and autonomous capture system of seafood based on underwater robot[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2393-2402. doi: 10.13700/j.bh.1001-5965.2019.0377(in Chinese)

基于水下机器人的海产品智能检测与自主抓取系统

doi: 10.13700/j.bh.1001-5965.2019.0377
基金项目: 

国家自然科学基金 61272368

国家自然科学基金 61370142

中央高校基本科研业务费专项资金 3132016352

交通运输部应用基础研究项目 2015329225300

大连市科技创新项目 2018J12GX037

详细信息
    作者简介:

    徐凤强  男, 博士研究生。主要研究方向:数字图像处理、水下目标检测、计算机视觉

    董鹏  男, 硕士研究生。主要研究方向:数字图像处理、计算机视觉

    王辉兵  男, 博士。主要研究方向:计算机视觉、机器学习

    付先平  男, 博士, 教授, 博士生导师。主要研究方向:多媒体技术、驾驶行为、模式识别、数字图像处理和视频图像低码率编码

    通讯作者:

    付先平, E-mail: fxp@dlmu.edu.cn

  • 中图分类号: V221+.3;TB553

Intelligent detection and autonomous capture system of seafood based on underwater robot

Funds: 

National Natural Science Foundation of China 61272368

National Natural Science Foundation of China 61370142

the Fundamental Research Funds for the Central Universities 3132016352

the Fundamental Research of Ministry of Transport of P. R. China 2015329225300

Dalian Science and Technology Innovation 2018J12GX037

More Information
  • 摘要:

    针对水下机器人实现自主抓取过程中缺乏引导系统的问题,提出了一种依托水下机器人的海产品智能检测与自主抓取系统,用来解决水下目标的智能检测问题,并引导水下机器人进行海产品的自主抓取。将卷积神经网络检测算法应用到水下场景,利用水下图像数据集训练特定的网络模型DSOD检测海产品。建立短基线定位系统定位水下作业的机器人。通过分析相机成像坐标系与定位系统坐标系之间的关系,提出了一种计算海产品实际位置的坐标转换方法,计算海产品的实际位置。设计了一种基于反馈机制的多信号分析方法,引导机器人在水下移动并抓捕海产品。为了验证所提系统的有效性,搭建了一款水下抓捕机器人,并成功将所提算法应用到机器人,在真实海洋环境中进行海产品的自主抓取实验。

     

  • 图 1  卷积神经网络模型的训练和水下目标实时检测

    Figure 1.  CNN model training and real-time marine object detection

    图 2  水声定位几何原理

    Figure 2.  Geometric principle of underwater sound positioning

    图 3  机器人与目标在定位系统中的位置关系

    Figure 3.  Relative location of robot and target in positioning system

    图 4  机器人世界坐标系和定位系统坐标系的关系

    Figure 4.  Relation between robot world coordinate system and positioning system coordinate system

    图 5  水下机器人在限制区域内的运动轨迹

    Figure 5.  Trajectory of underwater robot moving within constrained area

    图 6  基于反馈机制的多信号分析方法

    Figure 6.  Multi-signal analysis method based on feedback mechanism

    图 7  多信号分析方法流程

    Figure 7.  Flowchart of multi-signal analysis method

    图 8  检测模块和机器人之间的通信协议

    Figure 8.  Communication protocol between detection module and robot

    图 9  水下机器人组成结构

    Figure 9.  Underwater robot composition structure

    图 10  机器人完整的抓取动作

    Figure 10.  Robot's complete capture action

    图 11  水下目标检测结果

    Figure 11.  Detection results of marine object

    表  1  水下图像数据集检测结果对比

    Table  1.   Comparison of detection results on underwater image dataset

    检测算法 网络模型 速度/(帧·s-1) 准确率/%
    Faster R-CNN[5] VGGNet 7 79.6
    SSD300[9] VGGNet 47 77.9
    YoLov3[19] Darknet-53 71 78.4
    DSOD[1] DS/64-192-48-1 17 81.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-09
  • 录用日期:  2019-08-20
  • 网络出版日期:  2019-12-20

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