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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.<\/jats:p>","DOI":"10.1007\/s10845-023-02116-1","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T14:03:07Z","timestamp":1682431387000},"page":"1541-1569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Anticipatory analysis of AGV trajectory in a 5G network using machine learning"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-8604","authenticated-orcid":false,"given":"Alberto","family":"Mozo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislav","family":"Vakaruk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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