基于射频识别技术的工件定位系统设计与实现

doi:10.16731/j.cnki.1671-3133.2017.07.022

现代制造工程 ›› 2017, Vol. 442 ›› Issue (7): 109-113.doi: 10.16731/j.cnki.1671-3133.2017.07.022

基于射频识别技术的工件定位系统设计与实现

马宗正^1,2, 马海舒², 马涛¹, 杨安杰¹, 王新莉¹

1 河南工程学院动力机械与车辆工程研究所,郑州 451191;
2 挪威科技大学产品与质量工程系,特隆赫姆N-7491

收稿日期:2016-01-06 出版日期:2017-07-18 发布日期:2017-09-29
作者简介:马宗正,博士,副教授,主要研究方向为发动机零/部件的设计与试验研究。E-mail:zongzhengma@163.com
基金资助:
河南省教育技术装备和实践教育研究资助项目(201518);河南工程学院机械可靠性工程技术中心资助项目(01)

Application of RFID technology to the workpiece localization

Ma Zongzheng^1,2, Ma Haishu², Ma Tao¹, Yang Anjie¹, Wang Xinli¹

1 Department of Mechanical Engineering,Henan Institute of Engineering,Zhengzhou 451191,China;
2 Department of Production and Quality Engineering,Norwegian University of Science and Technology, Trondheim N-7491,Norway

Received:2016-01-06 Online:2017-07-18 Published:2017-09-29

摘要/Abstract

摘要： 在综合考虑成本和识别速度的基础上,提出了将射频识别技术应用于生产线上工件的定位。首先介绍射频识别技术,提出了基于射频识别技术的工件定位系统;之后对定位系统进行了硬件系统选型及基于MATLAB软件的神经网络识别系统的设计;最后在实验室条件下对该系统进行了验证。试验结果表明,该系统可以完成基于天线接收到的信号强度进行定位的功能,针对一60cm×50cm的区域,该系统的定位误差可以控制在4cm以内,最大欧几里得误差控制在3.9cm以内。

关键词: 定位, 射频识别, 神经网络, 定位精度

Abstract: The application of the Radio Frequency Identification (RFID) in the work piece on the production line is proposed considering the costs,recognition speed and et al.Firstly,the location system of the production line workpiece is designed based on the fundamental technology of the RFID.Then the system hardware selection and based on MATLAB neural network system are realized and the feasibility is verified in the laboratory experiments.The experimental results show that location function can be achieved based on the received signal strength of the antenna and the locating accuracy of the system can be controlled within 4cm for one 60cm × 50cm area while the maximum Euclid error is less than 3.9cm.

Key words: localization, radio frequency identification, neural network, localization accuracy

中图分类号:

TK11

马宗正, 马海舒, 马涛, 杨安杰, 王新莉. 基于射频识别技术的工件定位系统设计与实现[J]. 现代制造工程, 2017, 442(7): 109-113.

Ma Zongzheng, Ma Haishu, Ma Tao, Yang Anjie, Wang Xinli. Application of RFID technology to the workpiece localization[J]. Modern Manufacturing Engineering, 2017, 442(7): 109-113.

参考文献

[1] 张曙.工业4.0 和智能制造[J].机械设计与制造工程,2014,43(8):1-5.
[2] L H,D H,B P,et al.Survey of Wireless Indoor Positioning Techniques and Systems[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2007,37(6):1067-1080.
[3] K K.Efficient design of indoor positioning systems based on location fingerprinting.2005 International Conference on Wireless Networks,Communications and Mobile Computing[C].[S.l.]:IEEE,2005:181-186.
[4] 曹世华.室内定位技术和系统的研究进展[J].计算机系统应用,2013(9):1-5.
[5] O M.Improved supply chain performance through RFID technology:comparative case analysis of Metro Group and Wal-Mart[D].Waollongong:University of Waollongong,2012.
[6] Z L,G R,P B S.Applications of RFID technology and smart parts in manufacturing.ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference[C].USA:American Society of Mechanical Engineers,2004:123-129.
[7] R S.Radio frequency identification device (RFID) and real time location systems (RTLS) enhance nursing care delivery.Sigma Theta Tau International’s 25th International Nursing Research Congress[C].[S.l.]:STTI,2014.
[8] P S,P M,K D,et al.Automated Toll Collection System Using RFID[J].IOSR Journal of Computer Engineering,2013,9(2):61-66.
[9] H T,H C D,B B M,et al.Range-free localization schemes for large scale sensor networks.Proceedings of the 9th annual international conference on Mobile computing and networking[C].[S.l.]:ACM,2003:81-95.[10] LM N,L Y H.LANDMARC:indoor location sensing using active RFID[J].Wireless Networks,2004,10(6):701-710.
[11] 马歇尔.Visual Studio 2010并行编程从入门到精通[M].北京:清华大学出版社,2013.
[12] G M,C N,B R W.An artificial neural network approach to the problem of wireless sensors network localization[J].Robotics and Computer-Integrated Manufacturing,2013,29(1):96-109.
[13] H R.Theory of the backpropagation neural network.International Joint Conference on Neural Networks[C].[S.l.]:IEEE,1989:593-605.
[14] 康东,石喜勤,李勇鹏.射频识别(RFID) 核心技术与典型应用开发案例[M].北京:人民邮电出版社,2008.

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基于射频识别技术的工件定位系统设计与实现

Application of RFID technology to the workpiece localization

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