Advertisement

Collaborative and traditional robotic assembly: a comparison model

  • Maurizio FaccioEmail author
  • Matteo Bottin
  • Giulio Rosati
ORIGINAL ARTICLE
  • 21 Downloads

Abstract

In the last decade, robot manufacturers have started to produce collaborative industrial robots, that can work while safely sharing the workspace with a human operator. In this way, robot repeatability, combined with human dexterity, can move automated assembly to a new level of flexibility. The aim of this paper is to investigate the conditions at which such systems, called collaborative assembly systems (CAS), can be better performing than the traditional manual or automated assembly systems. Throughput and unit direct production cost are considered for the comparison. The estimation of such performance figures, which is straightforward in traditional automated assembly systems, becomes more complex in the case of collaborative systems. In fact, both task allocation between the human and the robot, and the way they collaborate/interfere with each other during assembly, affect the throughput of CAS. With the aim of taking into account such parameters, we introduce a set of system variables and a mathematical model which allow to estimate the real convenience of the implementation of CAS in the industrial scenario. In the paper, the model is applied to compare CAS to manual assembly and to noncollaborative automated assembly, both with parameters derived from the literature and in a case study. Finally, a set of implementation conditions is derived, related to the task allocation that maximises CAS performance.

Keywords

Collaborative robots Flexible assembly systems Convenience analysis Unit direct production cost Throughput 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

References

  1. 1.
    Wolfson W, Gordon SJ (1997) . Assem Autom 17(2):116CrossRefGoogle Scholar
  2. 2.
    Rosati G, Faccio M, Carli A, Rossi A (2013) . Assem Autom 33(1):8CrossRefGoogle Scholar
  3. 3.
    Barbazza L, Faccio M, Oscari F, Rosati G (2017) . Assem Autom 37(4):411CrossRefGoogle Scholar
  4. 4.
    Heilala J, Montonen J, Väätäinen O (2008) . Proc Inst Mech Eng B J Eng Manuf 222(10):1289CrossRefGoogle Scholar
  5. 5.
    Edmondson N, Redford A (2002) . Assem Autom 22(2):139CrossRefGoogle Scholar
  6. 6.
    Finetto C, Faccio M, Rosati G, Rossi A (2014) . Int J Adv Manuf Technol 70(5-8):797CrossRefGoogle Scholar
  7. 7.
    Rosati G, Faccio M, Finetto C, Carli A (2013) . Assem Autom 33(2):165CrossRefGoogle Scholar
  8. 8.
    Colgate JE, Wannasuphoprasit W, Peshkin MA (1996) .. In: Proceedings of the 1996 ASME international mechanical engineering congress and exposition (ASME)Google Scholar
  9. 9.
    Dong Y, Zhang L, Lu D, Bernbardt R, Surdilovic D (2004) .. In: Fifth world congress on intelligent control and automation, 2004. WCICA, vol. 5 (IEEE, 2004), pp 4635–4639Google Scholar
  10. 10.
    Ou J, Fussell SR, Chen X, Setlock LD, Yang J (2003) .. In: Proceedings of the 5th international conference on Multimodal interfaces (ACM), pp 242–249Google Scholar
  11. 11.
    McBean JM (2004) Design and control of a voice coil actuated robot arm for human-robot interaction. Ph.D. thesis Massachusetts Institute of TechnologyGoogle Scholar
  12. 12.
    Krüger J, Lien TK, Verl A (2009) . CIRP Ann Manuf Technol 58(2):628CrossRefGoogle Scholar
  13. 13.
    Hägele M, Schaaf W, Helms E (2002) .. In: Proceedings of the 33rd ISR (International Symposium on Robotics), vol 7Google Scholar
  14. 14.
    Chen F, Sekiyama K, Cannella F, Fukuda T (2014) . IEEE Trans Autom Sci Eng 11(4):1065CrossRefGoogle Scholar
  15. 15.
    Arai T, Kato R, Fujita M (2010) . CIRP Ann Manuf Technol 59(1):5CrossRefGoogle Scholar
  16. 16.
    Rahman SM, Ikeura R (2017) . IET Electr Power Appl 11(7):1235CrossRefGoogle Scholar
  17. 17.
    Matsas E, Vosniakos GC, Batras D (2018) . Robot Comput Integr Manuf 50:168CrossRefGoogle Scholar
  18. 18.
    Schmidt B, Wang L (2013) . Procedia CIRP 7:545CrossRefGoogle Scholar
  19. 19.
    Flacco F, Kröger T, De Luca A, Khatib O (2012) .. In: International conference on Robotics and Automation (ICRA). IEEE, pp 338–345Google Scholar
  20. 20.
    Bascetta L, Ferretti G, Rocco P, Ardö H, Bruyninckx H, Demeester E, Di Lello E (2011) .. In: 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 2971–2978Google Scholar
  21. 21.
    Huber M, Lenz C, Wendt C, Färber B, Knoll A, Glasauer S (2013) .. In: 2013 IEEE in RO-MAN, IEEE, pp 503–508Google Scholar
  22. 22.
    Morioka M, Sakakibara S (2010) . CIRP Ann Manuf Technol 59(1):9CrossRefGoogle Scholar
  23. 23.
    Lasota PA, Rossano GF, Shah JA (2014) .. In: 2014 IEEE international conference on automation science and engineering (CASE), IEEE, pp 339–344Google Scholar
  24. 24.
    Cherubini A et al (2016) . Robot Comput Integr Manuf 40:1CrossRefGoogle Scholar
  25. 25.
    Faccio M, Gamberi M, Bortolini M (2016) . Int J Prod Res 54(3):761CrossRefGoogle Scholar
  26. 26.
    Hu JJ, Huang CN, Wang HW, Shieh PH, Hu JS (2013) .. In: International conference on advanced robotics and intelligent systems (ARIS), IEEE, pp 28–31Google Scholar
  27. 27.
    Pellegrinelli S, Admoni H, Javdani S, Srinivasa S (2016) .. In: 2016 IEEE/RSJ International conference on intelligent robots and systems (IROS), IEEE, pp 831–838Google Scholar
  28. 28.
    Duan F, Tan JTC, Arai T (2011) .. In: 2011 30th Chinese in control conference (CCC), IEEE, pp 5468–5473Google Scholar
  29. 29.
    Green SA, Billinghurst M, Chen X, Chase JG (2008) . Int J Adv Robot Syst 5(1):1CrossRefGoogle Scholar
  30. 30.
    Wei K, Ren B (2018) . Sensors 18(2):571CrossRefGoogle Scholar
  31. 31.
    Coupeté E, Moutarde F, Manitsaris S (2018) Multi-users online recognition of technical gestures for natural humanrobot collaboration in manufacturing. Autonomous Robots. In press, pp 1–17Google Scholar
  32. 32.
    Brito T, Lima J, Costa P, Piardi L (2017) .. In: Iberian robotics conference (Springer), pp 643–654Google Scholar
  33. 33.
    Mauro S, Scimmi LS, Pastorelli S (2017) .. In: International conference on robotics in Alpe-Adria Danube Region (Springer), pp 344–352Google Scholar
  34. 34.
    Heydaryan S, Suaza Bedolla J, Belingardi G (2018) . Appl Sci 8(3):344CrossRefGoogle Scholar
  35. 35.
    Scholer M, Müller IR (2017) . IFAC-PapersOnLine 50(1):5694CrossRefGoogle Scholar
  36. 36.
    Wojtynek M, Oestreich H, Beyer O, Wrede S (2017) .. In: Proceedings of the 2017 IEEE/SICE international symposium on system integrationGoogle Scholar
  37. 37.
    Wu B, Hu B, Lin H (2017) .. In: American control conference (ACC), (IEEE, 2017), pp 1536–1541Google Scholar
  38. 38.
    Fantini P, Pinzone M, Sella F, Taisch M (2017) .. In: International conference on applied human factors and ergonomics (Springer), pp 259–268Google Scholar
  39. 39.
    Pearce M, Mutlu B, Shah J, Radwin R (2018) Optimizing makespan and ergonomics in integrating collaborative robots into manufacturing processes. IEEE Trans Autom Sci Eng 15(4):1772–1784CrossRefGoogle Scholar
  40. 40.
    Khalid A, Kirisci P, Khan ZH, Ghrairi Z, Thoben KD, Pannek J (2018) . Comput Ind 97:132CrossRefGoogle Scholar
  41. 41.
    Rosati G, Boschetti G, Biondi A, Rossi A (2009) . Opt Lasers Eng 47(3):320CrossRefGoogle Scholar
  42. 42.
    Rosati G, Faccio M, Barbazza L, Rossi A (2015) . Int J Adv Manuf Technol 81(5-8):1289CrossRefGoogle Scholar
  43. 43.
    Finetto C, Rosati G, Faccio M, Rossi A (2015) . Assem Autom 35(1):114CrossRefGoogle Scholar
  44. 44.
    Folkard S, Tucker P (2003) . Occup Med 53(2):95CrossRefGoogle Scholar
  45. 45.
    Nayak A, Reyes Levalle R, Lee S, Nof SY (2016) . Int J Prod Res 54(23):6969CrossRefGoogle Scholar
  46. 46.
    Müller C, Grunewald M, Spengler TS (2017) Redundant configuration of automated flow lines based on “Industry 4.0”-technologies. J Bus Econ 87(7):877–898CrossRefGoogle Scholar
  47. 47.
    EN ISO 10218-1: Safety requirements for industrial robots - Part 1: RobotsGoogle Scholar
  48. 48.
    EN ISO 10218-2: Safety requirements for industrial robots - Part 2: Robot systems and integrationGoogle Scholar
  49. 49.
    Sawyer: Rethink robotics unveils new robot. https://spectrum.ieee.org/automaton/robotics/industrial-robots/sawyer-rethink-robotics-new-robot. Accessed: 2017-11-16
  50. 50.
    Limère V, Landeghem HV, Goetschalckx M, Aghezzaf EH, McGinnis LF (2012) . Int J Prod Res 50(15):4046CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management and EngineeringUniversity of PadovaVicenzaItaly
  2. 2.Department of Management and EngineeringUniversity of PadovaPadovaItaly
  3. 3.Department of Industrial EngineeringUniversity of PadovaPadovaItaly

Personalised recommendations