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

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 AGV its PLC. Stopping an can result a loss tens thousands euros per minute therefore, use machine learning techniques anticipate behavior seems be appropriate. This work proposes application advanced deep neural networks forecast trajectory errors even if disturbances appear 5G network by capturing packets PLC-AGV connection not using any sensor user equipment (AGV or PLC), which facilitates real-time deployment solution. To demonstrate proposed solution, industrial virtualised PLC were deployed real network. Furthermore, set architectures was selected, extensive collection experiments designed analyse forecasting performance each architecture. Additionally, we discuss issues that appeared during execution best models open laboratory, provided realistic controlled scenario.