基于多超声传感器信息和NeuCube的移动机器人走廊场景识别

doi:10.11772/j.issn.1001-9081.2015.10.2833

计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2833-2837.DOI: 10.11772/j.issn.1001-9081.2015.10.2833

• 第十五届中国机器学习会议(CCML2015)论文 • 上一篇下一篇

基于多超声传感器信息和NeuCube的移动机器人走廊场景识别

王秀青¹, 侯增广², 潘世英¹, 谭民², 王永吉^3,4,5, 曾慧⁶

1. 河北师范大学应用信息技术系, 石家庄 050024;
2. 复杂系统管理与控制国家重点实验室(中国科学院自动化研究所), 北京 100190;
3. 基础软件国家工程研究中心(中国科学院软件研究所), 北京 100190;
4. 计算机科学国家重点实验室(中国科学院软件研究所), 北京 100190;
5. 中国科学院软件研究所互联网软件技术实验室, 北京 100190;
6. 北京科技大学信息学院, 北京 100083

收稿日期:2015-06-08 修回日期:2015-06-26 出版日期:2015-10-10 发布日期:2015-10-14
通讯作者: 王秀青(1970-),女,河北石家庄人,教授,博士,主要研究方向:智能计算、先进机器人,xqwang2013@163.com
作者简介:侯增广(1969-),男,河北临城人,研究员,博士生导师,博士,主要研究方向:先进机器人、智能控制、嵌入式系统;潘世英(1979-),女,河北石家庄人,讲师,硕士,主要研究方向:智能计算;谭民(1963-),男,广东新会人,研究员,博士生导师,博士,主要研究方向:机器人控制;王永吉(1963-),男,辽宁营口人,研究员,博士生导师,博士,CCF高级会员,主要研究方向:虚拟化技术、隐蔽信道、实时系统、人工智能、数据挖掘、软件工程;曾慧(1980-),女,山东菏泽人,副教授,博士,主要研究方向:机器视觉、模式识别。
基金资助:
国家自然科学基金资助项目(61175059,61375010);河北省自然科学基金资助项目(F2014205115);中国科学院复杂系统管理与控制国家重点实验室开放课题资助项目(20120105)。

Corridor scene recognition for mobile robots based on multi-sonar-sensor information and NeuCube

WANG Xiuqing¹, HOU Zengguang², PAN Shiying¹, TAN Min², WANG Yongji^3,4,5, ZENG Hui⁶

1. Department of Application Information Technology, Hebei Normal University, Shijiazhuang Hebei 050024, China;
2. State Key Laboratory of Management and Control for Complex System (Institute of Automation, Chinese Academy of Sciences), Beijing 100090, China;
3. National Engineering Research Center for Fundamental Software (Institute of Software, Chinese Academy of Sciences), Beijing 100190, China;
4. State Key Laboratory of Computer Science (Institute of Software, Chinese Academy of Sciences), Beijing 100190, China;
5. Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;
6. School of Information, University of Science and Technology Beijing, Beijing 100083, China

Received:2015-06-08 Revised:2015-06-26 Online:2015-10-10 Published:2015-10-14

摘要/Abstract

摘要： 为提高室内移动机器人的环境感知能力,针对其常处的结构化走廊场景的分类、Spiking神经网络(SNN)和基于SNN的新型计算模型NeuCube进行研究。SNN利用尖脉冲传递时、空信息,比传统的神经网络更适于动态、时序信息的分析,以及各种模式信息的识别和分类。此外,SNN更易于用硬件实现。在对NeuCube的基本原理、学习方法和计算步骤进行讨论的基础上,利用多超声传感信息和NeuCube对室内移动机器人常处的7种走廊场景进行识别。实验结果表明基于多超声传感信息和NeuCube的移动机器人走廊场景分类方法可以对7种走廊场景进行有效识别,该方法有助于增强移动机器人的自主性和提高其智能水平。

关键词: 移动机器人, 多超声传感信息, spiking 神经网络, 环境感知, 分类

Abstract: To improve the perception ability of indoor mobile robots, the classification method for the commonly structured corridor-scenes, Spiking Neural Network (SNN) and NeuCube, which is a novel computing model based on SNN, were studied. SNN can convey spatio-temporal information by spikes. Besides, SNN is more suitable for analyzing dynamic and time-series data, and for recognizing data of various patterns than traditional Neural Network (NN). It is easy to be implemented by hardware. The principle, learning methods and calculation steps of NeuCube were discussed. Then seven common corridor scenes were recognized by the classification method based on multi-sonar-sensor information and NeuCube. The experimental results show that the proposed method is effective. Additionally, it is helpful for improving autonomy and intelligence of mobile robots.

Key words: mobile robot, multi-sonar-sensor information, Spiking Neural Network (SNN), environment perception, classification

中图分类号:

TP242.6

王秀青, 侯增广, 潘世英, 谭民, 王永吉, 曾慧. 基于多超声传感器信息和NeuCube的移动机器人走廊场景识别[J]. 计算机应用, 2015, 35(10): 2833-2837.

WANG Xiuqing, HOU Zengguang, PAN Shiying, TAN Min, WANG Yongji, ZENG Hui. Corridor scene recognition for mobile robots based on multi-sonar-sensor information and NeuCube[J]. Journal of Computer Applications, 2015, 35(10): 2833-2837.

参考文献

[1] WANG Z, MA Y, GU J. Multi-focus image fusion using PCNN [J]. Pattern Recognition, 2010, 43(6): 2003-2016.
[2] LI X, MA Y, FENG X. Self-adaptive autowave pulse-coupled neural network for shortest-path problem [J]. Neurocomputing, 2013, 115: 63-71.
[3] MA Y, LIU L, ZHAN K, et al. Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval [J]. Image and Vision Computing, 2010, 28(11): 1524-1529.
[4] MAASS W. Networks of spiking neurons: the third generation of neural network models [J]. Neural Networks, 1997, 10(9):1659-1671.
[5] VREEKEN J. Spiking neural networks, an introduction [EB/OL]. [2015-01-01]. http://people.mmci.uni-saarland.de/~jilles/pubs/2002/spiking_neural_networks_an_introduction-vreeken.pdf.
[6] GERSTNER W. Spiking neurons [M]//MAASS W, BISHOP C M. Pulsed Neural Networks. Cambridge: MIT Press, 1999.
[7] GERSTNER W, KISTLER W. Spiking neuron models [M]. Cambridge: Cambridge University Press,2002.
[8] MAASS W. Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons [M]//MOZER M, JORDAN M I, PETSCHE T. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 1997: 211-217.
[9] GERSTNER W. Time structure of the activity in neural network models[J]. Physical Review E, 1995, 51: 738-758.
[10] KASABOV N, CAPECC E. Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes [J]. Information Sciences, 2015, 294:565-575.
[11] KASABOV N, FEIGI V, HOU Z G, et al. Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke [J]. Neurocomputing, 2014, 134: 269-279.
[12] TU E, CAO L, YANG J, et al. A novel graph-based k-means for nonlinear manifold clustering and representative selection [J]. Neurocomputing, 2014,143:109-122.
[13] KASABOV N, DHOBLE K, NUNTALID N. Dynamic evolving spiking neural networks for on-line spatio and spectrotemporal pattern recognition[J]. Neural Networks, 2012, 141: 188-201.
[14] WYSOSKI S, BENUSKOVA L, KASABOV N, et al. Evolving spiking neural networks for audiovisual information processing [J]. Neural Networks, 2010, 23(7): 819-835.
[15] WANG X, HOU Z, TAN M, et al. Spiking neural networks and its application in mobile robots [C]//Proceeding of 30th China Control Conference. Shanghai: Shanghai Science and Technology Press,2011:4133-4138.(王秀青,侯增广,谭民,等. Spiking神经网络及其在移动机器人中的应用[C]//第30届中国控制会议论文集.上海:上海科学技术出版社,2011:4133-4138.)
[16] CHENG L, HOU Z, TAN M, et al. Neural-network-based adaptive leader-following control for multi-Agent systems with uncertainties [J]. IEEE Transaction Neural Network, 2010, 21(8): 1351-1358.
[17] HOU Z, CHENG L, TAN M. Multi-criteria optimization for coordination of redundant robots using a dual neural network [J]. IEEE Transactions on System, Man and Cybernetics, 2010, 40(4):1075-1087.
[18] CHENG L, HOU Z, TAN M. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model [J]. Automatica, 2009, 45(10): 2312-2318.
[19] ONIZ Y, KAYNAK O. Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm [J]. Neurocomputing, 2015,149: 690-699.
[20] GAMEZ D. Information integration based predictions about the conscious states of a spiking neural network [J]. Consciousness and Cognition, 2010, 19(1): 294-310.
[21] WANG X, HOU Z, TAN M, et al. A behavior controller for mobile robot based on spiking neural networks [J]. Neurocomputing, 2008, 71: 655-666.
[22] WANG X, HOU Z, LV F, et al. Mobile robots' modular navigation controller using spiking neural networks [J]. Neurocomputing, 2014, 134: 230-238.
[23] KASABOV N. NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data[J]. Neural Networks, 2014, 52: 62-76.
[24] KASABOV N. NeuCube EvoSpike architecture for spatio-temporal modelling and pattern recognition of brain signals [EB/OL]. [2014-10-10]. http://ncs.ethz.ch/projects/evospike/publications/neucube.
[25] THORPE S, DELORME A. Spike-based strategies for rapid processing [J]. Neural Networks, 2001, 14(6/7): 715-725.
[26] THORPE S, GAUTRAIS J. Rank order coding [J]. Computational Neuroscience: Trends in Research,1998, 13: 113-119.
[27] MARKRA H, LUBKE J, FROTSCHER M, et al. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs [J]. Science, 1997, 5297(275): 213-215.
[28] FUSI S, ANNUNZIATO M, BADONI D, et al. Spike driven synaptic plasticity: theory, simulation, VLSI implementation [J]. Neural Computation, 2000, 12(10): 2227-2258.
[29] LICHTSTEINER P, DELBRUCK T. A 64×64 AER logarithmic temporal derivative silicon retina [J]. Research in Microelectronics and Electronics, 2005, 2: 202-205.
[30] WANG X. Research on environment perception and behavior control for mobile robot based on spiking neural networks [D]. Beijing: Chinese Academy of Sciences, Postgraduate Institute, 2007.(王秀青.基于Spiking神经网络的环境感知和行为控制的研究[D].北京:中国科学院研究生院, 2007.)
[31] WANG X, HOU Z, TAN M, et al. Corridor-scene classifying method for mobile robot based on multi-sonar-sensor information fusion [J]. International Journal of Information Acquisition, 2007, 4(1):15-26.

基于多超声传感器信息和NeuCube的移动机器人走廊场景识别

Corridor scene recognition for mobile robots based on multi-sonar-sensor information and NeuCube

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李开荣, 刘爽, 胡倩倩, 唐亦媛. 基于转角约束的改进蚁群优化算法路径规划[J]. 计算机应用, 2021, 41(9): 2560-2568.
[2]	宋中山, 梁家锐, 郑禄, 刘振宇, 帖军. 基于双向门控尺度特征融合的遥感场景分类[J]. 计算机应用, 2021, 41(9): 2726-2735.
[3]	毛铭泽, 曹芮浩, 闫春钢. 基于权值多样性的半监督分类算法[J]. 计算机应用, 2021, 41(9): 2473-2480.
[4]	胡天杰, 胡文军, 王士同. 分布熵惩罚的支持向量数据描述[J]. 计算机应用, 2021, 41(8): 2212-2218.
[5]	李蒙蒙, 秦伟, 刘艺, 刁兴春. 结合头脑风暴优化的混合蚁群优化算法[J]. 计算机应用, 2021, 41(8): 2412-2417.
[6]	朱亮, 徐华, 崔鑫. 基于基分类器系数和多样性的改进AdaBoost算法[J]. 计算机应用, 2021, 41(8): 2225-2231.
[7]	陆国庆, 孙昊. 基于随机行走的群机器人二维地图构建[J]. 计算机应用, 2021, 41(7): 2121-2127.
[8]	张洋, 江铭虎. 基于注意力机制的文本作者识别[J]. 计算机应用, 2021, 41(7): 1897-1901.
[9]	尹春勇, 张帼杰. 面向分布式漂移数据流的集成分类模型[J]. 计算机应用, 2021, 41(7): 1947-1955.
[10]	肖振远, 王逸涵, 罗建桥, 熊鹰, 李柏林. 基于部分加权损失函数的RefineDet[J]. 计算机应用, 2021, 41(7): 1928-1932.
[11]	章惠, 张娜娜, 黄俊. 优化LeNet-5网络的多角度头部姿态估计方法[J]. 计算机应用, 2021, 41(6): 1667-1672.
[12]	史杨潇, 章军, 陈鹏, 王兵. 基于轻量级网络的钢铁表面缺陷分类[J]. 计算机应用, 2021, 41(6): 1836-1841.
[13]	贾鹤鸣, 郎春博, 姜子超. 基于轻量级卷积神经网络的植物叶片病害识别方法[J]. 计算机应用, 2021, 41(6): 1812-1819.
[14]	韦铭燕, 陈彧, 张亮. 针对混合变量优化问题的协同进化蚁群优化算法[J]. 计算机应用, 2021, 41(5): 1412-1418.
[15]	陆鑫伟, 余鹏飞, 李海燕, 李红松, 丁文谦. 基于注意力自身线性融合的弱监督细粒度图像分类算法[J]. 计算机应用, 2021, 41(5): 1319-1325.