Automatic box sorting system using artificial intelligence and voice interaction

Jose Miguel Bejarano, Robinson Jiménez-Moreno, Javier Eduardo Martinez-Baquero

Abstract

This article compares three artificial intelligence algorithms in the development of an automatic box sorting system using a voice-based user interaction interface. The system classifies between four different box sizes in a virtual environment where it extracts the desired size from the production line, which is identified by voice from a user interface. With the advances in artificial intelligence techniques, it is necessary to contextualize and establish the applicability of each one to generate developments that do not focus on the most recent algorithm but on the optimal one that satisfies a design requirement. By comparing techniques such as nearest neighbors, artificial neural networks, and convolutional neural networks, the effectiveness of each technique in a simple classification task is evident. Given the low computational resource usage of the nearest neighbor algorithm and its reduced response times and computational cost, the advantage of traditional algorithms over advanced pattern recognition algorithms is evident, as they can be integrated with recent voice recognition algorithms in industrial-type virtual scenarios.

 

Keywords: Nearest neighbors, Artificial neural networks, Convolutional neural networks, Automatic speech recognition, Box classification, Virtual environment.

 

DOI https://doi.org/10.55463/issn.1674-2974.52.11.7


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CAO Y., XIANG W., WEI J., CAO S., TIAN X., ZHONG J., FANG X., LUO B., LYU H. and LI XIANGKUI. CrossFusionSleepNet: A multimodal deep learning model for automatic sleep stage classification. Biomedical Signal Processing and Control, 2026,112(PartA): 108538. https://doi.org/10.1016/j.bspc.2025.108538.

GUO B.E., SHEN Y., ZHOU Z.F., LIU X., WEI Y.X. and YANG L. Advanced deep learning for automatic classification of fired bullets from standard-issue firearms. Science & Justice, 2025,65(6): 101335. https://doi.org/10.1016/j.scijus.2025.101335.

NASIR S., SHEIKH S.A. and MALIK F.M. Automatic modulation classification using convolutional neural network and support vector machine. Digital Signal Processing, 2025,(64):105249. https://doi.org/10.1016/j.dsp.2025.105249

ARNELA M., VIDAÑA-VILA E., FANTINELLI A., MOÑUX-BERNAL A., VAQUERIZO-SERRANO J. and SOCORÓ J.C. Generation of ultrasonic and audible sound waves for the automatic classification of packaging waste in reverse vending machines. Waste Management, 2025,204: 114934. https://doi.org/10.1016/j.wasman.2025.114934.

HOSSAIN MD.M. and ROY K. The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging. Journal of Hazardous Materials, 2025,(484): 136702, https://doi.org/10.1016/j.jhazmat.2024.136702.

YILDIZ S.N., OKAY F.Y., ISLAMOV A. and ÖZDEMIR S. Improved Chain-based Multi-Output Classification for Packaging Planning. Procedia Computer Science, 2024,231: 697-702. https://doi.org/10.1016/j.procs.2023.12.159.

TEYE E., AMUAH C.L.Y., LAMPTEY F.P., OBENG F. and NYORKEH R. Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms. Smart Agricultural Technology,2024,(8):100453. https://doi.org/10.1016/j.atech.2024.100453.

DODAMPEGAMA S., HOU L., ASADI E., ZHANG G. and SETUNGE S. Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications. Resources, Conservation and Recycling, 2024,202:107375. https://doi.org/10.1016/j.resconrec.2023.107375.

MCENTEGGART F., RAMASUBRAMANIAN A.K, ZEINALI M. and PAPAKOSTAS N. Optimising robot motion planning through the integration of diverse process simulation tools. Procedia CIRP, 2025,136: 918-923. https://doi.org/10.1016/j.procir.2025.08.156.

AUGENSTEIN T.E., NAGALLA D., MOHACEY A., CUBILLOS L.H., LEE M.H., RANGANATHAN R. and KRISHNAN C. A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces. Computers in Biology and Medicine,2024,1781:108778. https://doi.org/10.1016/j.compbiomed.2024.108778.

FARIAS G., FABREGAS E., PERALTA E., TORRES E. and DORMIDO S. A Khepera IV library for robotic control education using V-REP. IFAC-PapersOnLine, 2017,50(1): 9150-9155. https://doi.org/10.1016/j.ifacol.2017.08.1721.

MARINI N., MARCHESIN S., FERRIS L.B., PÜTTMANN S., WODZINSKI M., FRATTI R., PODAREANU D., CAPUTO A., BOYTCHEVA S., VATRANO S., FRAGGETTA F., NAGTEGAAL I., SILVELLO G., ATZORI M. and MÜLLER H. Automatic labels are as effective as manual labels in digital pathology images classification with deep learning. Journal of Pathology Informatics,2025,18:100462. https://doi.org/10.1016/j.jpi.2025.100462.

KA H. Voice-Controlled Vision-based Semi-Autonomous Assistive Robotic Manipulation Assistance. Archives of Physical Medicine and Rehabilitation, 2015,96(10):e10-e11. https://doi.org/10.1016/j.apmr.2015.08.028.

CHAKRADEO V.K., MALHOTRA K., LEE M.R. and NATHAN C.A.O. Navigational Surgery with Voice: Controlled Robotic Assist for Endoscopic Approach to the Pituitary. Otolaryngology - Head and Neck Surgery, 2005,133(2),Supplement:P153. https://doi.org/10.1016/j.otohns.2005.05.344.

BAKOURI M. Development of Voice Control Algorithm for Robotic Wheelchair Using MIN and LSTM Models. Computers, Materials and Continua, 2022,73(2): 2441-2456. https://doi.org/10.32604/cmc.2022.025106.

MEGHANA M., USHA KUMARI CH., STHUTHI PRIYA J., MRINAL P., ABHINAV VENKAT SAI K., PRASHANTH REDDY S., VIKRANTH K., SANTOSH KUMAR T. and PANIGRAHY A.K. Hand gesture recognition and voice controlled robot. Materials Today: Proceedings,2020,33(Part7):4121-4123. https://doi.org/10.1016/j.matpr.2020.06.553.

PARTHA SARADI V. and KAILASAPATHI P. Voice-based motion control of a robotic vehicle through visible light communication. Computers & Electrical Engineering,2019,76:154-167. https://doi.org/10.1016/j.compeleceng.2019.03.011.

KADRI I., SELOUANI S.A., GHRIBI M., GHALI R. and MEKHOUKH S. LLM-driven agent for speech-enabled control of industrial robots: A case study in snow-crab quality inspection. Results in Engineering, 2025,27: 106660. https://doi.org/10.1016/j.rineng.2025.106660.

GUO Y., XU W., PRADHAN S., BRAVO C. and BEN-TZVI P. Personalized voice activated grasping system for a robotic exoskeleton glove. Mechatronics, 2022,83: 102745. https://doi.org/10.1016/j.mechatronics.2022.102745.

BARATTA A., CIMINO A., LONGO F. and NICOLETTI L. Digital twin for human-robot collaboration enhancement in manufacturing systems: Literature review and direction for future developments. Computers & Industrial Engineering,2024,187:109764. https://doi.org/10.1016/j.cie.2023.109764.

JIMÉNEZ-MORENO R. and ESPITIA-CUBILLOS A. A. Comparative Performance Analysis of Transformer and Convolutional Networks for Machine Vision-Oriented Mobile Robots. Journal of Hunan University Natural Sciences,2025,52(2):113-121. https://doi.org/10.55463/issn.1674-2974.52.2.11


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