Testing the vertical and cyber-physical integration of cognitive robots in manufacturing

Volker Krueger, Francesco Rovida, Bjarne Grossmann, Ronald Petrick, Matthew Crosby, Arnaud Charzoule, German Martin Garcia, Sven Behnke, Cesar Toscano, Germano Veiga

Research output: Contribution to journalArticle

10 Citations (Scopus)
76 Downloads (Pure)

Abstract

In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a new and simpler way of programming based on robot skills, automated task planning, and knowledge modeling, and (c) enabling the robots to function in a shared human/robot workspace with the ability to handle unexpected situations. The STAMINA project has built a robotic system that meets these objectives for an automotive kitting application, which has also been tested, validated, and demonstrated in a relevant environment (TRL6). This paper describes the STAMINA robot system and the evaluation of this system on a series of realistic kitting tasks. The structure of the system, evaluation methodology, and experimental results, are presented along with the insights and experiences gained from this work.

Original languageEnglish
Pages (from-to)213-229
Number of pages17
JournalRobotics and Computer-Integrated Manufacturing
Volume57
Early online date18 Dec 2018
DOIs
Publication statusPublished - Jun 2019

Keywords

  • Autonomous robot
  • Kitting
  • Robot skills

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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  • Cite this

    Krueger, V., Rovida, F., Grossmann, B., Petrick, R., Crosby, M., Charzoule, A., Martin Garcia, G., Behnke, S., Toscano, C., & Veiga, G. (2019). Testing the vertical and cyber-physical integration of cognitive robots in manufacturing. Robotics and Computer-Integrated Manufacturing, 57, 213-229. https://doi.org/10.1016/j.rcim.2018.11.011