Anticipatory analysis of AGV trajectory in a 5G network using machine learning

Alberto Mozo (), Stanislav Vakaruk, J. Enrique Sierra-García and Antonio Pastor
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Alberto Mozo: Universidad Politécnica de Madrid
Stanislav Vakaruk: Universidad Politécnica de Madrid
J. Enrique Sierra-García: Universidad de Burgos
Antonio Pastor: Universidad Politécnica de Madrid

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 7, 1569 pages

Abstract: Abstract A new generation of Automatic Guided Vehicles (AGV) virtualises their Programmable Logic Controller (PLC) in the cloud deploying 5G-based communication infrastructures to provide ultra-fast and reliable links between the AGV and its PLC. Stopping an AGV can result in a loss of tens of thousands of euros per minute and therefore, the use of machine learning techniques to anticipate AGV behavior seems to be appropriate. This work proposes the application of advanced deep neural networks to forecast AGV trajectory errors even if disturbances appear in the 5G network by capturing the packets of the PLC-AGV connection and not using any sensor in the user equipment (AGV or PLC), which facilitates the real-time deployment of the solution. To demonstrate the proposed solution, an industrial AGV and a virtualised PLC were deployed in a real 5G network. Furthermore, a set of advanced deep learning architectures was selected, and an extensive collection of experiments was designed to analyse the forecasting performance of each architecture. Additionally, we discuss the real-time issues that appeared during the execution of the best models in a 5G open laboratory, that provided a realistic deployment in a controlled scenario.

Keywords: Industry 4.0; 5G; Multi-access edge computing (MEC); Automatic Guided Vehicle (AGV); Transformers; Machine learning; Deep learning; Forecasting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02116-1

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