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KI - Künstliche Intelligenz

, Volume 33, Issue 2, pp 151–161 | Cite as

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

  • Michael WojtynekEmail author
  • Jochen Jakob Steil
  • Sebastian Wrede
Technical Contribution
  • 149 Downloads

Abstract

The transformation of today’s manufacturing lines into truly adaptive systems facilitating individualized mass production requires new approaches for the efficient integration, configuration and control of robotics and automation components. Recently, various types of Plug-and-Produce architectures were proposed that support the discovery, integration and configuration of field devices, automation equipment or industrial robots during commissioning or even operation of manufacturing systems. However, in many of these approaches, the configuration possibilities are limited, which is a particular problem if robots operate in dynamic environments with constrained workspaces and exchangeable automation components as typically required for flexible manufacturing processes. In this article, we introduce an extended Plug-and-Produce concept based on dynamic motion planning, co-simulation and a collaborative human-robot interaction scheme that facilitates the quick adaptation of robotics behaviors in the context of a modular production system. To confirm our hypothesis on the efficiency and usability of this concept, we carried out a feasibility study where participants performed a flexible workcell setup. The results indicate that the assistance and features for planning effectively support the users in tasks of different complexity and that a quick adaption is indeed possible. Based on our observations, we identify further research challenges in the context of Plug, Plan and Produce applied to smart manufacturing.

Keywords

Industry 4.0 Collaborative robots Smart workcells Plug-and-Produce 

Notes

Acknowledgements

This work received funding from the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster “Intelligent Technical Systems OstWestfalenLippe” (it’s OWL) managed by the Project Management Agency Karlsruhe (PTKA). We are grateful to the HARTING Technology Group supporting this work and contribution with the cooperation in the it’s OWL-FlexiMiR project.

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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research Institute for Cognition and Robotics (CoR-Lab)Bielefeld UniversityBielefeldGermany
  2. 2.Institute for Robotics and Process Control (IRP)BraunschweigGermany

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