“Programming Fine Manufacturing Tasks on Collaborative Robots: A Case Study on Industrial Gluing”

Authors: Daniel Schäle, Martin Fodstad Stølen and Erik Kyrkjebø,
Affiliation: Western Norway University of Applied Sciences
Reference: 2023, Vol 44, No 4, pp. 141-154.

Keywords: Cobots, Manufacturing, Robot programming, CAM, Learning from Demonstration

Abstract: This paper explores programming of fine manufacturing tasks using collaborative robots. We conduct a case study based on an industrial gluing task, comparing two programming approaches: Learning from Demonstration (LfD) and Computer-Aided Manufacturing (CAM). We investigate the suitability of these approaches for ad-hoc automation of fine manufacturing tasks by expert operators, and discuss the strengths and weaknesses associated with their usage. The case study reveals that there are benefits and limitations to both approaches. The CAM-based approach provides a precise path for execution without the need for robot programming expertise, but is strongly dependent on the quality of the gauging process. The LfD approach is intuitive and quick to set up, but is strongly dependent on the quality of the demonstrations. Our findings suggest that there is a potential for a hybrid solution combining the best of both approaches in a unified interface, and provide a foundation for future research on hybrid programming interfaces for fine manufacturing tasks using collaborative robots.

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  title={{Programming Fine Manufacturing Tasks on Collaborative Robots: A Case Study on Industrial Gluing}},
  author={Schäle, Daniel and Stølen, Martin Fodstad and Kyrkjebø, Erik},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}