CONCEPT OF A SELF-LEARNING WORKPLACE CELL FOR WORKER ASSISTANCE WHILE COLLABORATION WITH A ROBOT WITHIN THE SELF-ADAPTING-PRODUCTION-PLANNING-SYSTEM

Johanna Ender

j.ender@stud.hs-wismar.de
1Liverpool John Moores University, Faculty of Engineering and Technology, 2Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering (Germany)
http://orcid.org/0000-0002-6827-3270

Jan Cetric Wagner


Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering (Germany)
http://orcid.org/0000-0002-9236-2935

Georg Kunert


Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering (Germany)
http://orcid.org/0000-0002-7469-8435

Fang Bin Guo


Liverpool John Moores University, Faculty of Engineering and Technology (United Kingdom)
http://orcid.org/0000-0002-7442-7344

Roland Larek


Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering (Germany)
http://orcid.org/0000-0003-2823-6237

Thorsten Pawletta


Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering (Germany)
http://orcid.org/0000-0003-1740-6560

Abstract

 For some time, the focus of past research on industrial workplace designs has been the optimization of processes from the technological point of view. Since human workers have to work within this environment the design process must regard Human Factor needs. The operators are under additional stress due to the range of high dynamic processes and due to the integration of robots and autonomous operating machines. There have been few studies on how Human Factors influence the design of workplaces for Human-Robot Collaboration (HRC). Furthermore, a comprehensive, systematic and human-centred design solution for industrial workplaces particularly considering Human Factor needs within HRC is widely uncertain and a specific application with reference to production workplaces is missing. 

The research findings described in this paper aim the optimization of workplaces for manual production and maintenance processes with respect to the workers within HRC. In order to increase the acceptance of integration of human-robot teams, the concept of the Assisting-Industrial-Workplace-System (AIWS) was developed. As a flexible hybrid cell for HRC integrated into a Self-Adapting-Production-Planning-System (SAPPS) assists the worker while interaction. 


Keywords:

human-robot collaboration, human factors, post-optimised reinforcement learning algorithm, self-adapting-production-planning-system

Bannat A.: Ein Assistenzsystem zur digitalen Werker-Unterstützung in der industriellen Produktion. TU München, 2014.
  Google Scholar

Bauernhansl T., ten Hompel M., Vogel-Heuser B. (Eds.): Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien, Migration. Springer Vieweg, Wiesbaden 2014.
DOI: https://doi.org/10.1007/978-3-658-04682-8   Google Scholar

Braseth A. O.: Information-Rich Design: A Concept for Large-Screen Display Graphics: Design Principles and Graphic Elements for Real-World Complex Processes. Norwegian University of Science and Technology, 2015.
  Google Scholar

Bullinger H.-J.: Ergonomie: Produkt- und Arbeitsplatzgestaltung. Vieweg+Teubner Verlag, Wiesbaden 1994.
DOI: https://doi.org/10.1007/978-3-663-12094-0_13   Google Scholar

Bullinger-Hoffmann A. C., Mühlstedt J.: Homo Sapiens Digitalis – Virtuelle Ergonomie und digitale Menschmodelle. Springer Vieweg, Wiesbaden 2016.
DOI: https://doi.org/10.1007/978-3-662-50459-8   Google Scholar

Dostal W., Kamp A.-W., Lahner M., Seessle W. P.: Flexible Fertigungssysteme und Arbeitsplatzstrukturen. W. Kohlhammer GmbH, Stuttgart 1982.
  Google Scholar

Endsley M. R., Jones D. G.: Designing for situation awareness: An approach to user-centered design. CRC Press, Boca Raton 2011.
  Google Scholar

Freitag M., Molzow-Voit F., Quandt M., Spöttl G.: Aktuelle Entwicklung der Robotik und ihre Implikationen für den Menschen. In: Molzow-Voit F., Quandt M., Freitag M., Spöttl G. (Eds.): Robotik in der Logistik: Qualifizierung für Fachkräfte und Entscheider. Springer Gabler, Wiesbaden 2016, 9–20.
DOI: https://doi.org/10.1007/978-3-658-08575-9_2   Google Scholar

Goschke T.: Aktivationstheoretische Ansätze: Motivation, Emotion, Volition. TU Dresden, 2013.
  Google Scholar

Grendel H., Larek R., Riedel F., Wagner J. C.: Enabling manual assembly and integration of aerospace structures for Industry 4.0 – methods. New Production Technologies in Aerospace Industry: MIC Proceedings 2017, Hannover 2017.
DOI: https://doi.org/10.1016/j.promfg.2017.11.004   Google Scholar

Hackl B., Wagner M., Attmer L., Baumann D.: New Work: Auf dem Weg zur neuen Arbeitswelt: Management-Impulse, Praxisbeispiele, Studien. Springer Gabler, Wiesbaden 2017.
DOI: https://doi.org/10.1007/978-3-658-16266-5   Google Scholar

ten Hompel M., Henke M.: Logistik 4.0. in SpringerLink, Industrie 4.0 in Produktion, Automatisierung und Logistik. In: Bauernhansl T., ten Hompel M., Vogel-Heuser B. (Eds.): Anwendung, Technologien, Migration. Springer Vieweg, Wiesbaden 2014, 615–624.
DOI: https://doi.org/10.1007/978-3-658-04682-8_32   Google Scholar

Kunert G., Pawletta T.: Generating of Task-Based Controls for Joint-Arm Robots with Simulation-based Reinforcement Learning. SNE 28(4), 2018, 149–156.
DOI: https://doi.org/10.11128/sne.28.tn.10442   Google Scholar

Kunert G., Pawletta T.: Generierung von Steuerungen für Gelenkarmroboter mit simulationsbasiertem Reinforcement-Learning. 24. Symposium Simulationstechnik ASIM 2018, 56, 2018.
  Google Scholar

Larek R., Grendel H., Wagner J. C., Riedel F.: Industry 4.0 in manual assembly processes – a concept for real time production steering and decision making. Procedia CIRP 79, 2019, 165–169.
DOI: https://doi.org/10.1016/j.procir.2019.02.038   Google Scholar

Lee J. D., Wickens Ch. D., Liu Y., Boyle L. Ng: An introduction to human factors engineering: A beta version. CreateSpace Independent Publishing Platform, 2017.
  Google Scholar

Lorenz M., Rüßmann M., Strack R., Lueth K. L., Bolle M.: Man and Machine in Industry 4.0: How Will Technology Transform the Industrial Workforce Through 2025? BCC The Boston Consulting Group, 2015.
  Google Scholar

Michalos G. et al.: ROBO-PARTNER: Seamless Human-Robot Cooperation for Intelligent, Flexible and Safe Operations in the Assembly Factories of the Future. Procedia CIRP 23, 2014, 71–76.
DOI: https://doi.org/10.1016/j.procir.2014.10.079   Google Scholar

Molzow-Voit F., Quandt M., Freitag M., Spöttl G. (Eds.): Robotik in der Logistik: Qualifizierung für Fachkräfte und Entscheider. Springer Gabler, Wiesbaden 2016.
DOI: https://doi.org/10.1007/978-3-658-08575-9   Google Scholar

Norman D. A.: The design of everyday things. Basic Books, New York 2013.
  Google Scholar

Onnasch L., Maier X., Jürgensohn T.: Mensch-Roboter-Interaktion – Eine Taxonomie für alle Anwendungsfälle. Bundesanstalt für Arbeitsschutz und Arbeitsmedizin 2016.
  Google Scholar

Sanders E. B.-N., Stappers P. J.: Co-creation and the new landscapes of design. CoDesign 4(1), 2008, 5–18.
DOI: https://doi.org/10.1080/15710880701875068   Google Scholar

Spath D. et al. (Eds.): Produktionsarbeit der Zukunft – Industrie 4.0: Fraunhofer Verlag, 2013.
  Google Scholar

Stark J., Mota R. R.C., Sharlin E.: Personal Space Intrusion in Human-Robot Collaboration. Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction – HRI '18, 2018, 245–246.
DOI: https://doi.org/10.1145/3173386.3176998   Google Scholar

Vogel-Heuser B., Bauernhansl T., ten Hompel M.: Handbuch Industrie 4.0: Produktion. Springer-Verlag GmbH Deutschland, Berlin 2017.
DOI: https://doi.org/10.1007/978-3-662-45279-0   Google Scholar

Wagner J. C., Larek R., Nüchter A.: Der Maximalnetzplan als Neuinterpretation der Netzplantechnik. Proc. of Wismarer Wirtschaftsinformatik-Tage 11, 2018, 123–136.
  Google Scholar

Westkämper E., Spath D., Constantinescu C., Lentes J.: Digitale Produktion. Springer-Verlag Berlin Heidelberg, Berlin 2013.
DOI: https://doi.org/10.1007/978-3-642-20259-9   Google Scholar

Yerkes R. M., Dodson J. D.: The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18(5), 1908, 459–482.
DOI: https://doi.org/10.1002/cne.920180503   Google Scholar

Ziegler J.: Wearables im industriellen Einsatz: Befähigung zu mobiler IT-gestützter Arbeit durch verteilte tragbare Benutzungsschnittstellen. 2015.
  Google Scholar

Acatech: Innovationspotenziale der Mensch-Maschine-Interaktion. Herbert Utz Verlag GmbH, Munich 2016.
  Google Scholar

Acatech: Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0: Abschlussbericht des Arbeitskreises Industrie 4.0. Deutschlands Zukunft als Produktionsstandort sichern, Apr. 2013.
  Google Scholar

AiF Projekt GmbH: ZIM-Erfolgsbeispiel: Exakt montiert – sicher verpackt – zufriedene Kunden. Jan. 2018.
  Google Scholar

Bundesministerium für Arbeit und Soziales Abteilung Grundsatzfragen des Sozialstaats, der Arbeitswelt und der sozialen Marktwirtschaft: WEISS BUCH Arbeiten 4.0: Arbeit weiter denken. 2017.
  Google Scholar

Bundesministerium für Bildung und Forschung: Zukunft der Arbeit: Innovationen für die Arbeit von morgen. 2016.
  Google Scholar

Fraunhofer IAO: Arbeitswelten der Zukunft: Jahresbericht. Fraunhofer-Gesellschaft 2017.
  Google Scholar

International Ergonomics Association IEA, Definition and Domains of Ergonomics. https://www.iea.cc/ (Available: 25.02.2019).
  Google Scholar

IXDS Human Industries Venture: Without design, Industry 4.0 will fail: Six challenges where design accelerates successful digital transformation in manufacturing. 2018, https://www.ixds.com/without-design-industry-40-will-fail (Available: 27.06.2018).
  Google Scholar

KUKA AG: Hello Industrie 4.0 – we go digital. https://www.nebbiolo.tech/wp-content/uploads/KUKA-Industrie-4.0.pdf (Available: 19.06.2018).
  Google Scholar

Steelcase Inc.: 360°Focus_Creativity: Creativity, Work and the Physical Environment. 17-0005439, 2017.
  Google Scholar

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Published
2019-12-15

Cited by

Ender, J., Wagner, J. C., Kunert, G., Guo, F. B. ., Larek, R., & Pawletta, T. (2019). CONCEPT OF A SELF-LEARNING WORKPLACE CELL FOR WORKER ASSISTANCE WHILE COLLABORATION WITH A ROBOT WITHIN THE SELF-ADAPTING-PRODUCTION-PLANNING-SYSTEM. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(4), 4–9. https://doi.org/10.35784/iapgos.36

Authors

Johanna Ender 
j.ender@stud.hs-wismar.de
1Liverpool John Moores University, Faculty of Engineering and Technology, 2Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering Germany
http://orcid.org/0000-0002-6827-3270

Authors

Jan Cetric Wagner 

Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering Germany
http://orcid.org/0000-0002-9236-2935

Authors

Georg Kunert 

Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering Germany
http://orcid.org/0000-0002-7469-8435

Authors

Fang Bin Guo 

Liverpool John Moores University, Faculty of Engineering and Technology United Kingdom
http://orcid.org/0000-0002-7442-7344

Authors

Roland Larek 

Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering Germany
http://orcid.org/0000-0003-2823-6237

Authors

Thorsten Pawletta 

Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Faculty of Engineering Germany
http://orcid.org/0000-0003-1740-6560

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