The Analysis of scientific papers related to intelligent forklifts

Authors

DOI:

https://doi.org/10.53591/easi.v4i2/2602

Keywords:

Artificial intelligence, Documental analysis, Documents,, Intelligent forklifts, Inventories

Abstract

This document shows an analysis of documents related to intelligent forklifts, which seeks to identify the advances in the field and recognize the existing knowledge gap in the area to support the future design of a forklift that, based on artificial intelligence algorithms, can operate with certain autonomy and be called an intelligent forklift. The PRISMA 2020 guide is used for the search, selection, evaluation and summary of findings of the papers. We start with the search in the Scopus database, defining inclusion criteria to limit the study. The documents are characterized, then an analysis of their bibliographic networks is made using Wosviewer, after which their summaries are studied with Voyant, to succinctly present the main findings. It is concluded that it is important to promote collaborative work among authors, organizations and countries, to complement the advances in forklifts, their routing and movement planning using artificial intelligence tools, such as convolutional or transformer type networks.

Author Biographies

  • Anny Espitia-Cubillos, Military University Nueva Granada

    Performed her undergraduate studies in Industrial Engineering in the Universidad Militar Nueva Granada in 2002 and M.Sc. in Industrial Engineering from the Universidad de Los Andes in 2006. She is an Associate Professor on Industrial Engineering Program at Universidad Militar Nueva Granada, Bogotá, Colombia.

  • Robinson Jiménez-Moreno, Military University Nueva Granada

    Is an Electronic Engineer graduated from Universidad Distrital Francisco José de Caldas in 2002. He received a M.Sc. in Engineering from Universidad Nacional de Colombia in 2012 and Ph.D. in Engineering at Universidad Distrital Francisco José de Caldas in 2018. His current working as associate professor of Universidad Militar Nueva Granada and research focuses on the use of convolutional neural networks for object recognition and image processing for robotic applications such as human-machine interaction. 

  • Esperanza Rodríguez-Carmona, Military University Nueva Granada

    Performed her undergraduate studies in Mechanical Engineering (1997) from the Universidad Tecnológica de Pereira (UTP) and Master's degree in university teaching from the Universidad de La Salle, Bogotá, Colombia. She is an Associate Professor on Industrial Engineering program at Universidad Militar Nueva Granada, Bogotá, Colombia.

References

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Arfianto, A., Wahyudi, S., Hidayat, T., & Ismail, M. (2019). Unmanned vehicles use received signal strength indicator (RSSI) in instant beverage industry. 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA) (pp. 340–343). Batu, Indonesia. https://doi.org/10.1109/ICAMIMIA47173.2019.9223401

Ballamajalu, R., Kumar, P., & Ramesh, C. (2020). Turn and orientation sensitive A* for autonomous vehicles in intelligent material handling systems. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) (pp. 606–611). Kong, China. https://doi.org/10.1109/CASE48305.2020.9216869

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Chew, J., Lee, K., Wong, S., & Tan, H. (2019). Development of a virtual environment to realize human-machine interaction of forklift operation. 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA) (pp. 112–118). Daejeon, South Korea. https://doi.org/10.1109/RITAPP.2019.8932837

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Published

2025-11-04

Issue

Vol. 4 No. 2 (2025): [Special issue] CIIA 2025 - International Conference of Applied Industrial Engineering: Intelligent Models and Data Engineering

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Special Issue

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Copyright (c) 2025 Anny Espitia-Cubillos, Robinson Jiménez-Moreno, Esperanza Rodríguez-Carmona

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How to Cite

The Analysis of scientific papers related to intelligent forklifts. (2025). EASI: Engineering and Applied Sciences in Industry, 4(2), 9-17. https://doi.org/10.53591/easi.v4i2/2602