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基于改进型自主发育网络的机器人场景识别方法

余慧瑾 方勇纯

余慧瑾,  方勇纯.  基于改进型自主发育网络的机器人场景识别方法.  自动化学报,  2021,  47(7): 1530−1538 doi: 10.16383/j.aas.c180779
引用本文: 余慧瑾,  方勇纯.  基于改进型自主发育网络的机器人场景识别方法.  自动化学报,  2021,  47(7): 1530−1538 doi: 10.16383/j.aas.c180779
Yu Hui-Jin,  Fang Yong-Chun.  A robot scene recognition method based on improved autonomous developmental network.  Acta Automatica Sinica,  2021,  47(7): 1530−1538 doi: 10.16383/j.aas.c180779
Citation: Yu Hui-Jin,  Fang Yong-Chun.  A robot scene recognition method based on improved autonomous developmental network.  Acta Automatica Sinica,  2021,  47(7): 1530−1538 doi: 10.16383/j.aas.c180779

基于改进型自主发育网络的机器人场景识别方法

doi: 10.16383/j.aas.c180779
基金项目: 国家自然科学基金(61873132)资助
详细信息
    作者简介:

    余慧瑾:南开大学机器人与信息自动化研究所硕士研究生. 2018年获得电子科技大学计算机科学与工程学院信息安全专业学士学位. 主要研究方向为机器视觉及发育神经网络.E-mail: 18920952389@163.com

    方勇纯:南开大学机器人与信息自动化研究所教授. 2002年获得美国克莱姆森大学博士学位. 主要研究方向为显智能机器人与非线性系统控制. 本文通信作者.E-mail: fangyc@nankai.edu.cn

A Robot Scene Recognition Method Based on Improved Autonomous Developmental Network

Funds: Supported by National Natural Science Foundation of China (61873132)
More Information
    Author Bio:

    YU Hui-Jin Master student at the Institute of Robotics and Automatic Information System, Nankai University. She received her bachelor degree in information security from the School of Computer Science and Engineering, University of Electronic Science and Technology of China in 2018. Her research interest covers machine vision and development network

    FANG Yong-Chun Professor at Institute of Robotics and Automatic Information System, Nankai University, China. He received his Ph.D. degree in electrical engineering from Clemson University, USA in 2002. His research interest covers intelligent robot and nonlinear system control. Corresponding author of this paper

  • 摘要:

    场景识别是移动机器人在陌生动态环境中完成任务的前提. 考虑到现有方法的不足, 本文提出了一种基于改进型自主发育网络的场景识别方法, 它通过引入基于多优胜神经元的Top-k竞争机制、基于负向学习的权值更新、基于连续性样本的加强型学习等步骤实现对场景的快速识别, 并使该方法具有更好的适应能力. 对于这种基于改进型自主发育网络的场景识别方法, 通过实验进行了对比测试. 结果表明, 这种改进型自主发育神经网络节点利用率高, 场景识别准确可靠, 可以较好地满足机器人作业的实际需求.

  • 图  1  改进型自主发育网络框架

    Fig.  1  Improved autonomous developmental network framework

    图  2  改进型自主发育神经网络模型

    Fig.  2  Improved autonomous developmental neural network model

    图  3  改进型发育网络进行场景识别的算法流程

    Fig.  3  Algorithm flow for scene recognition in improved developmental network

    图  4  部分场景样本图像

    Fig.  4  Partial scene sample images

    图  5  Y层神经元激活个数对比

    Fig.  5  Comparison of the number of activations in layer Y neurons

    图  6  不同测试样本识别正确率对比

    Fig.  6  Comparison of correct test rates for different test samples

    图  7  场景识别正确率对比

    Fig.  7  Scene recognition correct rate comparison

    图  8  部分室内场景样本图像

    Fig.  8  Partial indoor scene sample image

    表  1  不同室内场景类别识别准确率

    Table  1  Classification accuracy of different indoor scenes

    不同室内场景类别实验室教室会议室
    识别准确率92.4%83.23%87.07%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-21
  • 录用日期:  2019-11-16
  • 网络出版日期:  2021-07-27
  • 刊出日期:  2021-07-27

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