Selection of optimal machine learning algorithm for autonomous guided vehicle’s control in a smart manufacturing environment

Abstract

Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.