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

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

余慧瑾^{1, 2,},
方勇纯^{1, 2,}

1.
南开大学机器人与信息自动化研究所天津 300071
2.
南开大学天津市智能机器人技术重点实验室天津 300071

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

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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

A Robot Scene Recognition Method Based on Improved Autonomous Developmental Network

YU Hui-Jin^{1, 2
,},
FANG Yong-Chun^{1, 2
,}

1.
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300071
2.
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300071

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

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.
- 机器人 /
- 场景识别 /
- 改进型自主发育神经网络
Abstract:
Scene recognition is a prerequisite for mobile robots to complete tasks in unfamiliar dynamic environments. Considering the drawbacks of existing methods, this paper proposes a scene recognition method based on improved autonomous developmental network, which introduces important steps, such as Top-k competition mechanism based on multi-winning neurons, weight update based on negative learning, and reinforcement learning with continuous sample images, into the basic algorithm, so as to efficiently achieve scene recognition tasks with good adaptability to various environment. To test the performance of the scene recognition method based on improved autonomous developmental network, numerous comparative experiments are implemented, with the obtained results showing that the improved autonomous developmental neural network model presents such advantages as high node utilization rate, reliable scene recognition results, and it thus better meets the practical needs of robot operations.
- Robot /
- scene recognition /
- improved autonomous developmental neural network

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图 1 改进型自主发育网络框架

Fig. 1 Improved autonomous developmental network framework

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图 2 改进型自主发育神经网络模型

Fig. 2 Improved autonomous developmental neural network model

下载: 全尺寸图片幻灯片

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

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图 4 部分场景样本图像

Fig. 4 Partial scene sample images

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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

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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

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图 7 场景识别正确率对比

Fig. 7 Scene recognition correct rate comparison

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图 8 部分室内场景样本图像

Fig. 8 Partial indoor scene sample image

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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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