An Approach for adapting a Cobot Workstation to Human Operator within a Deep Learning Camera

• Main Authors: De Miguel Lazaro, Olatz, Mohammed, Wael M., Ferrer, Borja Ramis, Bejarano, Ronal, Martinez Lastra, Jose L.
• Format: Conference Proceeding
• Language: English
• Subjects: cobots; deep learning; face recognition; human robot collaboration; pose detection
• ISSN: 2378-363X
• DOI: 10.1109/INDIN41052.2019.8972238

One of the major objectives of international projects in the field of Industrial Automation is to achieve a proper and safe human-robot collaboration. This will permit the coexistence of both humans and robots at factory shop floors, where each one has a clear role along the industrial processes. It's a matter of fact that machines, including robots, have specific features that determine the kind of operation(s) that they can perform better. Similarly, human operators have a set of skills and knowledge that permits them to accomplish their tasks at work. This article proposes the adaptation of robots to the skills of human operators in order to implement an efficient, safe and comfortable synergy between robots and humans that are working at the same workspace. As a representative case of study, this research work describes an approach for adapting a cobot workstation to human operators within an installed deep learning camera on the cobot. First, the camera is used to recognize the human operator that collaborates with the robot. Then, the corresponding profile is processed and serves as an input to a module in charge of adapting specific features of the robot. In this manner, the robot can adapt e.g., to the speed of operation according to the skills of the worker or deliver parts to be manipulated according to the handedness of the human worker. In addition, the deep learning camera is used for stopping the process at any time that the worked leaves unexpectedly the workstation.