3D Goods allocation in warehouse with L-GEM based 3-D RFID positioning

Author

Ng, Wing W Y ; Lin, Li ; Chan, Patrick P K ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

1

fYear

2011

fDate

10-13 July 2011

Firstpage

324

Lastpage

329

Abstract

Radio frequency identification (RFID) technology has a wide range of industrial applications because of its low cost. In this paper, RFID is used for indoor object positioning system and we focus on the scenario of goods allocation in a warehouse. An Radial Basis Function Neural Network (RBFNN) is trained via a minimization of the Localized Generalization Error (L-GEM) to learn the object location based on received RFID signals from multiple RFID readers. Goods are stacked in 3-Dimensional ways in a warehouse, the RBFNN outputs 3-D vectors as the predicted locations of target goods. The proposed method is robust to uncertainty and changes in environment. Using MATLAB simulations, the experimental result shows that the proposed method yields an efficient indoor positioning.

Keywords

goods distribution; radial basis function networks; radiofrequency identification; warehouse automation; 3D RFID positioning; 3D goods allocation; L-GEM; MATLAB simulations; RFID readers; RFID signals; indoor object positioning system; localized generalization error; radial basis function neural network; radio frequency identification; warehouse; Accuracy; Cybernetics; Machine learning; Neurons; Noise; Radiofrequency identification; Training; 3D Indoor Positioning; L-GEM; RBFNN; RFID;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016745

Filename

6016745