Applying RFID and NLP for efficient warehouse picking

Article type: Research Article

Authors: Xu, Man | Wang, Yunze^{; *} | Xing, Dan

Affiliations: Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang, Hebei, China

Correspondence: [*] Corresponding author: Yunze Wang, Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang, Hebei, 051430, China. E-mail: mumu009812@163.com.

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.

Keywords: RFID, natural language processing, speech recognition, warehouse picking, LANDMARC algorithm, indoor positioning

DOI: 10.3233/RFT-230055

Journal: International Journal of RF Technologies, vol. Pre-press, no. Pre-press, pp. 1-18, 2024