Arquitetura com Computação na Borda e Controle Preditivo Baseado em Modelo para Controle de Veículos Autoguiados

Abstract

Automated Guided Vehicles (AGVs) are essential for industry material transportation. In the Industry 4.0 and Industrial Internet of Things scenario, edge computing combined with new generation networks may support the navigation control of AGVs. Centralizing on edge not only facilitates the integration of the control system with the other factory systems but also reduces the cost of vehicles and battery consumption once the edge computing allocates the tasks which require more computing power. From the results of previous experiments, we found that Model-Based Predictive Control, together with edge computing and wireless networks, is a robust control solution for AGVs. The Model Predictive Control’s predictive nature can keep vehicles stable even in delays or packet loss on the network. Besides that, this approach promotes free navigation without fixed paths, which reduces costs and facilitates layout changes. Therefore, based on previous results and literature review, we propose an architecture for AGVs control with edge computing using the Model Predictive Control in this paper. The proposed architecture can support the AGVs navigation on smart factories of Industry 4.0, also in cases of network signal degradation.