Mobile robot decision-making system based on deep machine learning

Abstract

One of the major aspects of Industry 4.0 is enabling the manufacturing entities to operate in the dynamical systems autonomously. Therefore, to be autonomous, manufacturing entities need to have sensors to perceive their environment and utilize that information to make decisions regarding their actions. Having that in mind, in this paper, the authors propose a mobile robot decision-making system based on the integration of visual data and mobile robot pose. Mobile robot pose (current position and orientation) is integrated with two images gathered by two cameras and utilized to predict the possibility of gripping the part to be manufactured. A decision-making system is created by utilizing the deep learning model Resnet18 with an additional input for the mobile robot pose. The model is trained end-to-end and experimental evaluation is performed by using the mobile robot RACIO (Robot with Artificial Intelligence based COgnition).