Advertisement

Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells

  • Giulia Bruno
  • Dario Antonelli
ORIGINAL ARTICLE
  • 130 Downloads

Abstract

The rise of interest in collaborative robotic cells for assembly or manufacturing has been attested by their inclusion among the enabling technologies of Industry 4.0. In collaborative cells, robots work side by side with human operators allowing to address a larger production scope characterized by medium production volumes and significant product variability. Despite the advances in research and the availability of suitable industrial robot models, several open problems still exist, due to the shift in the way of working: correct assessment of the economic profitability, definition of a suitable process plan, task assignment to humans and robots, intuitive and fast robot programming. This paper addresses the task assignment problem by proposing a method for the classification of tasks starting from the hierarchical decomposition of production activities. Task classification is employed for workload distribution and detailed activity planning. The method relays on the assumption that tasks should be allocated, exploiting the different skills and assets of humans and robots, regardless of workload balancing. The proposed method was firstly tested on a simplified assembly process executed in laboratory, then it has been applied to the redesign of an actual industrial process.

Keywords

Human-robot collaboration Man-machine system Industry 4.0 Automation Flexible manufacturing systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

References

  1. 1.
    Antonelli D, Astanin S, Bruno G (2016) Applicability of human-robot collaboration to small batch production, Collaboration in a Hyperconnected World. IFIP Adv Inf Commun Technol 480:24–32CrossRefGoogle Scholar
  2. 2.
    Abi Research (2015) Collaborative robotics market exceeds 1 billion dollars by 2020. Retrieved from https://www.abiresearch.com/press/collaborative-robotics-market-exceeds-us1-billion-/. Accessed: 20 July 2017
  3. 3.
    Helms E, Schraft RD, Hagele M (2002) rob@ work: robot assistant in industrial environments. In: Robot and human interactive communication, pp 399–404Google Scholar
  4. 4.
    Michalos G, Makris S, Papakostas N, Mourtzis D, Chryssolouris G (2010) Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach. CIRP J Manuf Sci Technol 2 (2):81–91CrossRefGoogle Scholar
  5. 5.
    Hägele M, Nilsson K, Pires JN, Bischoff R (2016). In: Springer handbook of robotics. Springer International Publishing, Industrial robotics, pp 1385–1422Google Scholar
  6. 6.
    Womack JP, Jones DT, Roos D (1991) The machine that changed the world: the story of lean production. Harper Collins, New YorkGoogle Scholar
  7. 7.
    Tan JTC, Duan F, Zhang Y, Watanabe K, Kato R, Arai T (2009) Human-robot collaboration in cellular manufacturing: design and development. In: IEEE/RSJ international conference intelligent robots and systems, pp 29–34Google Scholar
  8. 8.
    Kazerooni H (1990) Human-robot interaction via the transfer of power and information signals. IEEE Trans Syst Man Cybern 20:450–463CrossRefGoogle Scholar
  9. 9.
    Luh JYS, Hu S (1998) Comparison of various models of robot and human in human-robot interaction. IEEE Int Conf Syst Man Cybern 2:1139–1144Google Scholar
  10. 10.
    Breazeal C et al (1998) A motivational system for regulating human-robot interaction. In: AAAI/IAAI, pp 54–61Google Scholar
  11. 11.
    Kang SB, Ikeuchi K (1995) Toward automatic robot instruction from perception-temporal segmentation of tasks from human hand motion. IEEE Trans Robot Autom 11:670–681CrossRefGoogle Scholar
  12. 12.
    Yang J, Xu Y, Chen CS (1997) Human action learning via hidden Markov model. IEEE Trans Syst Man Cybern 27:34–44CrossRefGoogle Scholar
  13. 13.
    Klingspor V, Demiris J, Kaiser M (1997) Human-robot communication and machine learning. Appl Artif Intell 11:719–746Google Scholar
  14. 14.
    Goodrich MA, Schultz AC (2007) Human-robot interaction: a survey. Found Trends Hum-Comput Interact 1:203–275CrossRefzbMATHGoogle Scholar
  15. 15.
    Bell CJ, Shenoy P, Chalodhorn R, Rao RP (2008) Control of a humanoid robot by a noninvasive brain–computer interface in humans. J Neural Eng 5(2):214CrossRefGoogle Scholar
  16. 16.
    Mavrikios D, Papakostas N, Mourtzis D, Chryssolouris G (2013) On industrial learning and training for the factories of the future: a conceptual, cognitive and technology framework. J Intell Manuf 24(3):473–485CrossRefGoogle Scholar
  17. 17.
    Mavridis N (2015) A review of verbal and non-verbal human–robot interactive communication. Robot Auton Syst 63:22–35MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kim S, Laschi C, Trimmer B (2013) Soft robotics: a bioinspired evolution in robotics. Trends Biotechnol 31(5):287–294CrossRefGoogle Scholar
  19. 19.
    Nguyen-Tuong D, Peters J (2011) Model learning for robot control: a survey. Cogn Process 12(4):319–340CrossRefGoogle Scholar
  20. 20.
    Almada-Lobo F (2016) The Industry 4.0 revolution and the future of manufacturing execution systems (MES). J Innov Manag 3(4):16–21Google Scholar
  21. 21.
    Morioka M, Sakakibara S (2011) A new cell production assembly system with human–robot cooperation. CIRP J Manuf Sci Technol 59:9–12CrossRefGoogle Scholar
  22. 22.
    Papakostas N, Michalos G, Makris S, Zouzias D, Chryssolouris G (2011) Industrial applications with cooperating robots for the flexible assembly. Int J Comput Integr Manuf 24(7):550– 560CrossRefGoogle Scholar
  23. 23.
    Pedrocchi N, Vicentini F, Malosio M, Tosatti LM (2012) Safe human–robot cooperation in an industrial environment. Int J Adv Robot Syst 10(27)Google Scholar
  24. 24.
    Ding H, Schipper M, Bjoern M (2014) Optimized task distribution for industrial assembly in mixed human–robot environments—case study on IO module assembly. In: IEEE international conference on automation science and engineeringGoogle Scholar
  25. 25.
    Harper C, Virk G (2010) Towards the development of international safety standards for human robot interaction. Int J Soc Robot 2(3):229–234CrossRefGoogle Scholar
  26. 26.
    Matthias B et al (2011) Safety of collaborative industrial robots: certification possibilities for a collaborative assembly robot concept. In: IEEE ISAMGoogle Scholar
  27. 27.
    Antonelli D, Bruno G (2017) Dynamic task sharing strategy for adaptive human-robot collaborative workcell. In: 24th international conference on production researchGoogle Scholar
  28. 28.
    Tan JTC, Duan F, Zhang Y, Arai T (2008) Extending task analysis in HTA to model man-machine collaboration in cell production. In: Proceedings of the IEEE international conference on robotics and biomimetics, Bangkok, ThailandGoogle Scholar
  29. 29.
    Tsarouchi P, Matthaiakis AS, Makris S, Chryssolouris G (2016) On a human-robot collaboration in an assembly cell. Int J Comput Integr Manuf 30(6):580–589CrossRefGoogle Scholar
  30. 30.
    Quinlan JR (2014) C4.5: programs for machine learning. Elsevier, San FranciscoGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Politecnico di TorinoTurinItaly

Personalised recommendations