Flexible Human-robot interaction: collaborative robot integrated with hand tracking

The rising demand for adaptable and user-friendly forms of interaction in the field of manufacturing and assembly tasks has led to increased attention on human-robot collaboration. Collaborative robots (cobots) have emerged as a promising solution to address this demand. In this study, we propose the integration and application of cobots along with a pre-trained deep learning model to assist users in assembly activities, specifically part handover and storage. The human-robot interaction is facilitated through a hand tracking system that enables a close approach to the user's hand. Testing on the system yielded 99% accuracy. The incorporation of deep learning models in cobot applications holds substantial potential for industry transformation, with implications spanning manufacturing, healthcare, and assistive technologies. This research serves as a compelling proof of concept, showcasing the effective implementation of deep learning models to facilitate close human-robot interactions.