Skip to main content
SpringerLink
Log in

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

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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 480:24–32

    Article  Google Scholar 

  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. Helms E, Schraft RD, Hagele M (2002) rob@ work: robot assistant in industrial environments. In: Robot and human interactive communication, pp 399–404

  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):81–91

    Article  Google Scholar 

  5. Hägele M, Nilsson K, Pires JN, Bischoff R (2016). In: Springer handbook of robotics. Springer International Publishing, Industrial robotics, pp 1385–1422

  6. Womack JP, Jones DT, Roos D (1991) The machine that changed the world: the story of lean production. Harper Collins, New York

    Google Scholar 

  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 29–34

  8. Kazerooni H (1990) Human-robot interaction via the transfer of power and information signals. IEEE Trans Syst Man Cybern 20:450–463

    Article  Google Scholar 

  9. Luh JYS, Hu S (1998) Comparison of various models of robot and human in human-robot interaction. IEEE Int Conf Syst Man Cybern 2:1139–1144

    Google Scholar 

  10. Breazeal C et al (1998) A motivational system for regulating human-robot interaction. In: AAAI/IAAI, pp 54–61

  11. Kang SB, Ikeuchi K (1995) Toward automatic robot instruction from perception-temporal segmentation of tasks from human hand motion. IEEE Trans Robot Autom 11:670–681

    Article  Google Scholar 

  12. Yang J, Xu Y, Chen CS (1997) Human action learning via hidden Markov model. IEEE Trans Syst Man Cybern 27:34–44

    Article  Google Scholar 

  13. Klingspor V, Demiris J, Kaiser M (1997) Human-robot communication and machine learning. Appl Artif Intell 11:719–746

    Google Scholar 

  14. Goodrich MA, Schultz AC (2007) Human-robot interaction: a survey. Found Trends Hum-Comput Interact 1:203–275

    Article  MATH  Google Scholar 

  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):214

    Article  Google Scholar 

  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):473–485

    Article  Google Scholar 

  17. Mavridis N (2015) A review of verbal and non-verbal human–robot interactive communication. Robot Auton Syst 63:22–35

    Article  MathSciNet  Google Scholar 

  18. Kim S, Laschi C, Trimmer B (2013) Soft robotics: a bioinspired evolution in robotics. Trends Biotechnol 31(5):287–294

    Article  Google Scholar 

  19. Nguyen-Tuong D, Peters J (2011) Model learning for robot control: a survey. Cogn Process 12(4):319–340

    Article  Google Scholar 

  20. Almada-Lobo F (2016) The Industry 4.0 revolution and the future of manufacturing execution systems (MES). J Innov Manag 3(4):16–21

    Google Scholar 

  21. Morioka M, Sakakibara S (2011) A new cell production assembly system with human–robot cooperation. CIRP J Manuf Sci Technol 59:9–12

    Article  Google Scholar 

  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– 560

    Article  Google Scholar 

  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)

  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 engineering

  25. Harper C, Virk G (2010) Towards the development of international safety standards for human robot interaction. Int J Soc Robot 2(3):229–234

    Article  Google Scholar 

  26. Matthias B et al (2011) Safety of collaborative industrial robots: certification possibilities for a collaborative assembly robot concept. In: IEEE ISAM

  27. Antonelli D, Bruno G (2017) Dynamic task sharing strategy for adaptive human-robot collaborative workcell. In: 24th international conference on production research

  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, Thailand

  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):580–589

    Article  Google Scholar 

  30. Quinlan JR (2014) C4.5: programs for machine learning. Elsevier, San Francisco

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giulia Bruno & Dario Antonelli

Authors
  1. Giulia Bruno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dario Antonelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giulia Bruno.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bruno, G., Antonelli, D. Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells. Int J Adv Manuf Technol 98, 2415–2427 (2018). https://doi.org/10.1007/s00170-018-2400-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-2400-4

Keywords

Search

Navigation