Integration of Deep Learning and Collaborative Robot for Assembly Tasks

Abstract

Human–robot collaboration has gained attention in the field of manufacturing and assembly tasks, necessitating the development of adaptable and user-friendly forms of interaction. To address this demand, collaborative robots (cobots) have emerged as a viable solution. Deep Learning has played a pivotal role in enhancing robot capabilities and facilitating their perception and understanding of the environment. This study proposes the integration of cobots and Deep Learning to assist users in assembly tasks such as part handover and storage. The proposed system includes an object classification system to categorize and store assembly elements, a voice recognition system to classify user commands, and a hand-tracking system for close interaction. Tests were conducted for each isolated system and for the complete application as used by different individuals, yielding an average accuracy of 91.25%. The integration of Deep Learning into cobot applications has significant potential for transforming industries, including manufacturing, healthcare, and assistive technologies. This work serves as a proof of concept for the use of several neural networks and a cobot in a collaborative task, demonstrating communication between the systems and proposing an evaluation approach for individual and integrated systems.

Voice-Controlled Robotics in Early Education: Implementing and Validating Child-Directed Interactions Using a Collaborative Robot and Artificial Intelligence

“…This capability underscores the potential for incorporating sophisticated robotics into early childhood educational environments. Through this implementation, we demonstrate that collaborative robotics, typically reserved for industrial settings (e.g., [14,15]), are equally effective and engaging in early education, fostering a comprehensive approach that encourages young learners to explore and understand complex concepts across various knowledge domain.…”