Research Article | OPEN ACCESS
Fuzzy Neural Network based RFID Positioning and Navigation Method for Mobile Robots
Bo-Wen Hong, Ying-Jeh Huang, Chu-Yung Chen, Ping-Chou Wu and Wei-Chung Chen
Department of Electrical Engineering, Yuan Ze University, Chungli, Taiwan
Research Journal of Applied Sciences, Engineering and Technology 2013 7:1233-1239
Received: October 30, 2012 | Accepted: December 21, 2012 | Published: July 05, 2013
Abstract
This study proposes the Radio Frequency Identification (RFID) indoor positioning and navigation method based on fuzzy neural network. The proposed method is applied to a wheelchair home health care robot with wireless communication. One reader and four tags are used. Based on the Received Signal Strength Indication (RSSI) data, the position of the robot can be determined. Further, to overcome the measurement error problem due to environmental parameter variation, a Fuzzy Neural Network (FNN) is proposed to compensate the measurement data. The FNN automatically adjust the weight, the variance and the mean value to overcome effectively the environmental parameter variation. A back-propagation algorithm is developed to achieve self-learning. The successful experiment results show that the proposed system architecture and positioning system provide satisfactory accuracy and make home health care wheelchair robot positioning system available for navigation and guidance.
Keywords:
Fuzzy neural network, indoor positioning, RFID, RSSI, wheelchair robot,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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