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

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