Human-Robot Interactive System for Warehouses using Speech SLAM and Deep Learning-based Barcode Recognition

This paper presents an initial investigation of a speech-based human-robot interaction system for locating items in a warehouse environment. The system uses a 2D SLAM map and visual servoing with deep learning-based barcode recognition to identify and locate items based on user speech commands. The system was tested with and without item location in the SLAM map and achieved a 100% success rate in identifying and localizing items. The average speech processing time was recorded at 9.28 seconds, and the system demonstrated a best-case timing of 15.46 seconds and a worst-case timing of 3 minutes for identifying items on different tables. The proposed system has the potential to improve the employment opportunities and experiences of blind or visually impaired workers in the warehouse industry. Future work will focus on testing the system in real-world environments and improving its performance in cluttered and dynamic settings.