Document Type
Article
Source Publication Title
The PErvasive Technologies Related to Assistive Environments Conference
DOI
https://dl.acm.org/doi/10.1145/3652037.3652061
Abstract
This paper presents an initial investigation of a speech-based humanrobot 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.
Publication Date
6-26-2024
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Nambiappan, Harish Ram; Nikanfar, Sama; Roy, Ayon; Hussain, Joey; Shinglot, Deep; Acharya, Sneh; Gans, Nicholas; and Makedon, Fillia, "Human-Robot Interactive System for Warehouses using Speech SLAM and Deep Learning-based Barcode Recognition" (2024). Association of Computing Machinery Open Access Agreement Publications-Archive. 102.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/102