Affiliation:
1. School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2. Guangzhou Jiaoxintou Technology Co., Ltd., Guangzhou 510100, China
3. Guangzhou Institute of Industrial Intelligence, Guangzhou 511458, China
Abstract
This paper proposes a tag position perception method for scenarios such as package retrieval in unmanned warehouses and book management in libraries. This method can accurately predict the distribution of tag space positions in real–time during RFID robot inventory. Firstly, the signal strength (RSSI) and speed of identification (SoI) are used as features. The grey wolf optimization multi–layer perceptron neural network model (GWO–MLP) is employed to predict the distance of tag groups. Secondly, a tag orientation prediction algorithm is designed to estimate the orientation of the tag groups. Finally, the periodicity of the phase is determined by the characteristic of RSSI attenuation as the tag–to–antenna distance increases, solving the problem of position ambiguity caused by phase periodicity. The experiment has shown that this method achieves a high accuracy rate of 96.67% and 97% in predicting the distance and orientation of tag groups, respectively. The average error in distance perception for the single tag is less than 3 cm, enabling precise perception of RFID tag positions. This method facilitates more efficient operation management and accurate item traceability.
Funder
Key Industry Innovation Chain Project of Shaanxi Province
Science and Technology Plan Project of Shaanxi Province
Key Research and Development plan of Shaanxi Province
Scientific Research Program Funded by Shaanxi Provincial Education Department
Science and Technology Plan Project of Xi’an
Graduate Innovation Fund of Xi’an University of Posts and Telecommunications
Guangzhou Nansha District Innovation Team Project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering