Production-oriented companies that aspire to the concept of Industry 4.0 primarily focus on the increasing flexibility and reconfigurability of the entire manufacturing system. By integrating a robotic material transport/handling system that features a high level of efficiency, flexibility, and intelligence, the entire manufacturing system reaps the benefits. With that in mind, the authors propose a deep learning-based visual servoing algorithm for the intelligent control of a wheeled mobile robot. By utilizing a visual servoing algorithm, mobile robotic systems can flexibly and efficiently adapt their trajectories to real-world conditions. Moreover, deep learning algorithms allow mobile robots to learn robust visual features that make visual servoing even more applicable. The authors utilize state-of-the-art deep learning models to train the mobile robot to perform visual servoing even without distinct features that are necessary for such a system to function properly. Experimental evaluation with the own developed mobile robot RAICO – Robot with Artificial Intelligence based COgnition has shown the benefits of the proposed visual control algorithm.