Affiliations: [a] Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA
| [b] Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
| [c] Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
Correspondence:
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Corresponding author: Kyoung-Yun Kim, Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA. E-mail: kykim@wayne.edu.
Abstract: The need for flexible production has turned manufacturing’s attention to integrate fast and uncomplicated solutions. Collaborative robots (cobots) have been considered the most impactful technology due to their versatility and human-robot interaction feature. Its implementation requires expertise in both process and cobot programming. Consequently, demand for effective programming training has increased over the past years. This paper, then, aims to design and explore a smart cobot programming system and conduct an empirical study to understand human engagement and programming performance. A repertory grid is employed based on cobot experts to understand different cobot programming approaches. Meaningful insights were considered to design and implement a smart programming system configuration. Then, an empirical programming study was performed considering cobot expertise and human engagement. Results demonstrated similarities and disparities in data collected, which was inferred to indicate differences in cobot programming behavior. Finally, the work identifies and discusses patterns to differentiate programmer expertise levels and behaviors.
