AI-Driven Factory Robot Remote Control Monitoring Equipment Using Machine Learning and Cloud-Based System
DOI:
https://doi.org/10.54536/ajsts.v5i2.8114Keywords:
Cloud Computer Networks, Embedded System, Factory Equipment, Iot, Network SecurityAbstract
Researcher on this study suggest robot agent work builds remotely, works in the system with cloud-based system for remote monitoring and control system in the domains of industrial automation and intelligent scenarios. The system is based on Xbee architecture, and clients can access it anytime and anywhere. Clients can remotely monitor and control the testing machine in real time through the cloud. After experimental verification, the real-time monitoring and control messages delay is 5 sec, which can meet the actual needs of remote. This remote monitoring agent control system fixed to the server control system, can improves the automation of the machine. And improves the working environment of the experimenters. Researcher on this survey used virtual software remotely Arduino board controlling the system implying a broader connectivity and integration between decentralized industrial and business.
Downloads
References
Caggiano, A., Segreto, T., & Teti, R. (2016). Cloud Manufacturing Framework for Smart Monitoring of Machining. Procedia CIRP, 55, 248-253. https://doi.org/https://doi.org/10.1016/j.procir.2016.08.049
Consortium, I. I. (2017). Architecture Alignment and Interoperability: An Industrial Internet Consortium and Platform Industrie 4.0 Joint Whitepaper. Industrial Internet Consortium: Needham, MA, USA.
Esmaeilzadeh, K. (2021). Remote Monitoring and Control of Industrial Equipment Through OPC UA and Cloud Computing. Master’s Thesis.
Hua, L., Da, X., Jian, Z., & Fuquan, Z. (2016). Design of a State Monitoring System for Equipment based on the Zigbee Wireless Sensor Network. International Journal of Online Engineering, 12(6).
Kandala, S. V., Gureja, A., Walchatwar, N., Agrawal, R., Sinha, S., Chaudhari, S., Vaidhyanathan, K., Choppella, V., Bhimalapuram, P., Kandath, H., & Hussain, A. (2025). Engineering End-to-End Remote Labs Using IoT-Based Retrofitting. IEEE Access, 13, 1106-1132. https://doi.org/10.1109/ACCESS.2024.3523066
Li, Q., Yang, Y., & Jiang, P. (2023). Remote Monitoring and Maintenance for Equipment and Production Lines on Industrial Internet: A Literature Review. Machines, 11(1), 12.
Liu, C., Jiang, P., & Jiang, W. (2020). Web-based digital twin modeling and remote control of cyber-physical production systems. Robotics and Computer-Integrated Manufacturing, 64, 101956. https://doi.org/https://doi.org/10.1016/j.rcim.2020.101956
Liu, Y., & Xiao, F. (2021). Intelligent Monitoring System of Residential Environment Based on Cloud Computing and Internet of Things. IEEE Access, 9, 58378-58389. https://doi.org/10.1109/ACCESS.2021.3070344
Pereira, J. (2009). Monitoring and Control: Today’s Market and Its Evolution till 2020. Luxembourg: Office for Official Publications of the European Communities.
Qi, H., Zhao, J., Sun, L., Cang, T., Li, Y., & Wang, X. (2022, 11-13 Nov. 2022). Design of a multi-channel fatigue test monitoring cloud platform based on the Internet of Things. 2022 6th International Symposium on Computer Science and Intelligent Control (ISCSIC),
Salkin, C., Oner, M., Ustundag, A., & Cevikcan, E. (2018). A Conceptual Framework for Industry 4.0. In A. Ustundag & E. Cevikcan (Eds.), Industry 4.0: Managing The Digital Transformation (pp. 3-23). Springer International Publishing. https://doi.org/10.1007/978-3-319-57870-5_1
Vanegas-Guillén, O., Parra-Rosero, P., Muñoz-Antón, J. M., Zumba-Gamboa, J., & Dillon, C. (2023). Remote Labs Meet Computational Notebooks: An Architecture for Simplifying the Workflow of Remote Educational Experiments. IEEE Access, 11, 132496-132515. https://doi.org/10.1109/ACCESS.2023.3336287
Wang, G., Xu, T., Wang, D., Cheng, P., Shao, C., Feng, F., & Zhou, P. (2024). Cloud-Based Remote Real-Time Monitoring and Control System for Spring Fatigue Testing Machine. Machines, 12(7), 462.
Wang, L. (2013). Machine availability monitoring and machining process planning towards Cloud manufacturing. CIRP Journal of Manufacturing Science and Technology, 6(4), 263-273. https://doi.org/https://doi.org/10.1016/j.cirpj.2013.07.001
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517-527. https://doi.org/https://doi.org/10.1016/j.jmsy.2015.04.008
Yin, Y., Li, Y., & Zhou, Z. D. (2014). Cloud Manufacturing: definitions, features, modes and core issues. Applied Mechanics and Materials, 563, 342-346.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Anas A.Nicola
This work is licensed under a Creative Commons Attribution 4.0 International License.
