Applying RFID and NLP for efficient warehouse picking

Author:

Xu Man1,Wang Yunze1,Xing Dan1

Affiliation:

1. Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang, Hebei, China

Abstract

 This paper proposes an intelligent warehouse-picking approach using radio frequency identification (RFID) indoor positioning and natural language processing (NLP) speech recognition. A forward maximum matching algorithm segments speech into domain terminology. Location was estimated by RFID signal strengths between reference tags and pickers. Simulation results demonstrated a 50% reduction in segmentation runtime versus conventional methods. Speech recognition accuracy reached 90–95%, improving by 23% over baseline. Positioning accuracy also increased substantially. The techniques can reduce picking errors and costs. Further work should evaluate performance in real-world environments.

Publisher

IOS Press

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