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Integration of Deep Learning and Collaborative Robot for Assembly Tasks

2024; Multidisciplinary Digital Publishing Institute; Volume: 14; Issue: 2 Linguagem: Inglês

10.3390/app14020839

2076-3417

Enrico Méndez, Oscar Ochoa, David Olivera-Guzman, Victor Hugo Soto-Herrera, José Alfredo Luna-Sánchez, Carolina Lucas-Dophe, Eloina Lugo-del-Real, Ivo N. Ayala-García, Miriam Alvarado, Alejandro González,

Digital Transformation in Industry

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.