Industrial Robots and Collaborative Robots: A Comparative Study

Abstract

In the era of Industry 4.0 and advancements in artificial intelligence tools, the industry is increasingly moving towards automation and reducing human intervention in production processes. In this context, industrial robots play a crucial role in modern industry, not only enhancing production and improving product quality but also addressing production uncertainties and, most importantly, reducing maintenance and production costs. To this end, the present paper proposes a comparative study of industrial robots and collaborative robots, based on a review of the literature published over the past five years. This analysis revealed that both types of robots share similarities in the nature of the faults encountered. However, the type of applications and their working environments require distinct strategies: early predictive maintenance is necessary for collaborative robots, while preventive maintenance is more suitable for industrial robots. These approaches rely on classification methods such as k-Nearest Neighbors algorithms, Convolutional Neural Networks, Random Forests, Support Vector Machines, and other neural networks for fault detection and effective trajectory control. The findings of this study will provide valuable information and serve as a significant resource for future researchers addressing challenges related to the use of these robots.