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Industrial and Medical Cyber-Physical Systems: Tackling User Requirements and Challenges in Robotics

  • Tamás HaideggerEmail author
  • Gurvinder S. Virk
  • Carol Herman
  • Roger Bostelman
  • Péter Galambos
  • György Györök
  • Imre J. Rudas
Chapter
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 14)

Abstract

Robotics is one of the major megatrends unfolding these days. Clearly, robots are capable of doing much more outside the factories than ever imagined, and that has a great impact on the whole society. This chapter provides some practical updates and guidelines on a few exciting aspects of automated technologies: applied robotics in the industry, in service and personal use and in the operating theaters, performing not only teleoperated surgeries but complex, delicate procedures as well. However, building reliable autonomous systems is not easy, and for another while, human operators will be required as a fallback option. Ensuring the safety of such hybrid control systems is complex, and requires novel human–machine interfaces. Situation awareness remains a key issue, keeping humans in the loop. Arguably, the social robotic sector is growing much faster than any industrial one, and as predicted, there soon will be robots in every household and around.

Notes

Acknowledgements

Thank are expressed to the robot standardization work groups at the various SODs. Authors acknowledge the financial support of this work by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project.

References

  1. 1.
    T. Jacobs, J. Veneman, G.S. Virk, T. Haidegger, The flourishing landscape of robot standardization. IEEE Robot. Autom. Mag. 25(1), 8–15 (2018)CrossRefGoogle Scholar
  2. 2.
    A. Takacs, I. Rudas, D. Bosl, T. Haidegger, Highly automated vehicles and self-driving cars. IEEE Robot. Autom. Mag. 25(4), 106–112 (2018)CrossRefGoogle Scholar
  3. 3.
    G.S. Virk, C. Herman, R. Bostelman, T. Haidegger, Challenges of the changing robot markets, in Nature-Inspired Mobile Robotics (2013), pp. 833–840Google Scholar
  4. 4.
    V.O. Robotics, A roadmap for US robotics: from internet to robotics, in Robotics Virtual Organization, 2nd edn. (2013)Google Scholar
  5. 5.
    J.W. Mroszczyk, Safety Practices for Automated Guided Vehicles (AGVs) (American Society of Safety Engineers, 2004)Google Scholar
  6. 6.
    R. Bischoff, U. Huggenberger, E. Prassler, Kuka youBot-a mobile manipulator for research and education, in 2011 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2011), pp. 1–4Google Scholar
  7. 7.
    L. Márton, Z. Szántó, T. Haidegger, P. Galambos, J. Kövecses, Internet-based bilateral teleoperation using a revised time-domain passivity controller. Acta Polytech. Hung. (2017)Google Scholar
  8. 8.
    B. Takács, R. Dóczi, B. Sütő, J. Kalló, T.A. Várkonyi, T. Haidegger, M. Kozlovszky, Extending AUV response robot capabilities to solve standardized test methods. Acta Polytech. Hung. 13(1), 157–170 (2016)Google Scholar
  9. 9.
    G.S. Virk, T. Haidegger, Classification guidelines for personal care robots–medical and non-medical applications, in Proceedings of the IEEE IROS Workshop on Safety in Human-Robot Coexistence & Interaction (2012), pp. 33–36Google Scholar
  10. 10.
    A. Takács, D.Á. Nagy, I. Rudas, T. Haidegger, Origins of surgical robotics: from space to the operating room. Acta Polytech. Hung. 13(1), 13–30 (2016)Google Scholar
  11. 11.
    M. Hoeckelmann, I.J. Rudas, P. Fiorini, F. Kirchner, T. Haidegger, Current capabilities and development potential in surgical robotics. Int. J. Adv. Rob. Syst. 12(5), 61 (2015)CrossRefGoogle Scholar
  12. 12.
    T. Haidegger, B. Benyó, L. Kovács, Z. Benyó, Force sensing and force control for surgical robots, in 7th IFAC Symposium on Modeling and Control in Biomedical Systems, vol. 7, no. 1, pp. 413–418, Aug 2009Google Scholar
  13. 13.
    Á. Takács, I. Rudas, T. Haidegger, Open-source research platforms and system integration in modern surgical robotics. Acta Univ. Sapientiae; Electr. Mech. Eng. 14(6), 20–34 (2015)Google Scholar
  14. 14.
    J.A. Marvel, Performance metrics of speed and separation monitoring in shared workspaces. IEEE Trans. Autom. Sci. Eng. 10(2), 405–414 (2013)CrossRefGoogle Scholar
  15. 15.
    J.A. Falco, J.A. Marvel, R.J. Norcross, Collaborative robotics: measuring blunt force impacts on humans. Chest 140(210), 45 (2012)Google Scholar
  16. 16.
    J. Marvel, R. Bostelman, Towards mobile manipulator safety standards, in 2013 IEEE International Symposium on Robotic and Sensors Environments (ROSE) (IEEE, 2013), pp. 31–36Google Scholar
  17. 17.
    R. Bostelman, R. Norcross, J. Falco, J. Marvel, Development of standard test methods for unmanned and manned industrial vehicles used near humans, in Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, vol. 8756 (International Society for Optics and Photonics, 2013), p. 87560PGoogle Scholar
  18. 18.
    Development of Standard Test Methods for Emergency Response Robots for Department of Homeland Security, Science and Technology Directorate (DHS S&T) and National Institute of Justice, NIST 2013. http://www.nist.gov/el/isd/ms/robottestmethods.cfm
  19. 19.
    T. Haidegger, I.J. Rudas, From concept to market: surgical robot development, in Human-Computer Interaction: Concepts, Methodologies, Tools, and Applications (IGI Global, 2016), pp. 484–522Google Scholar
  20. 20.
    J.B. Stiehl, J. Bach, D.A. Heck, Validation and metrology in CAOS, in Navigation and MIS in Orthopedic Surgery (Springer, Berlin, Heidelberg, 2007), pp. 68–78Google Scholar
  21. 21.
    A. Barrera, J. Bach, P. Kazanzides, H. Haider, Validation of an ASTM standard proposed to assess localizer functionality of CAOS systems: a joint effort by three laboratories, in Proceedings of the 20th Annual Congress of International Society for Technology in Arthroplasty (ISTA), Paris (2007), pp. 81–81Google Scholar
  22. 22.
    J.C. Chiao, J.M. Goldman, D.A. Heck, P. Kazanzides, W.J. Peine, J.B. Stiehl et al., Metrology and standards needs for some categories of medical devices. J. Res. Natl. Inst. Stand. Technol. 113(2), 121 (2008)CrossRefGoogle Scholar
  23. 23.
    A. Gartner, Teleneurology and requirements of the Medical Devices Directive (MDD) (Baaske Medical GmbH & Co., Lübbecke, 2008), pp. 1–22Google Scholar
  24. 24.
    D.B. Kaber, M.R. Endsley, The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theor. Issues Ergon. Sci. 5(2), 113–153 (2004)CrossRefGoogle Scholar
  25. 25.
    B. Fei, W.S. Ng, S. Chauhan, C.K. Kwoh, The safety issues of medical robotics. Reliab. Eng. Syst. Saf. 73(2), 183–192 (2001)CrossRefGoogle Scholar
  26. 26.
    P. Varley, Techniques for development of safety-related software for surgical robots. IEEE Trans. Inf. Technol. Biomed. 3(4), 261–267 (1999)CrossRefGoogle Scholar
  27. 27.
    J. Guiochet, A. Vilchis, Safety analysis of a medical robot for tele-echography, in 2nd IARP IEEE/RAS Joint Workshop on Technical Challenge for Dependable Robots in Human Environments, Toulouse, France, Oct 2002Google Scholar
  28. 28.
    S.Y. Nof (ed.), Handbook of Industrial Robotics, vol. 1 (John Wiley & Sons, 1999)Google Scholar
  29. 29.
    P. Grunert, K. Darabi, J. Espinosa, R. Filippi, Computer-aided navigation in neurosurgery. Neurosurg. Rev. 26(2), 73–99 (2003)CrossRefGoogle Scholar
  30. 30.
    G. Kronreif, Robot systems for percutaneous needle placement, in Proceedings of the 1st BME–MAVE International Computer-Integrated Surgery Workshop, Budapest (2011)Google Scholar
  31. 31.
    D.V. Makarov, J.B. Yu, R.A. Desai, D.F. Penson, C.P. Gross, The association between diffusion of the surgical robot and radical prostatectomy rates. Med. Care 333–339 (2011)Google Scholar
  32. 32.
    G.S. Virk, Safety standard for personal care robots, in Mobile Robotics: Solutions and Challenges (2010), pp. 147–154Google Scholar
  33. 33.
    Food and Drug Administration, De novo classification process (evaluation of automatic class III designation) (2017)Google Scholar
  34. 34.
    A. Bannat, T. Bautze, M. Beetz, J. Blume, K. Diepold, C. Ertelt et al., Artificial cognition in production systems. IEEE Trans. Autom. Sci. Eng. 8(1), 148–174 (2011)CrossRefGoogle Scholar
  35. 35.
    B. Matthias, S. Kock, H. Jerregard, M. Källman, I. Lundberg, Safety of collaborative industrial robots: certification possibilities for a collaborative assembly robot concept, in 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM) (IEEE, 2011), pp. 1–6Google Scholar
  36. 36.
    A.M. Zanchettin, N.M. Ceriani, P. Rocco, H. Ding, B. Matthias, Safety in human-robot collaborative manufacturing environments: metrics and control. IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2016)CrossRefGoogle Scholar
  37. 37.
    J.A. Marvel, R. Bostelman, Test methods for the evaluation of manufacturing mobile manipulator safety. JRM 28(2), 199–214 (2016)CrossRefGoogle Scholar
  38. 38.
    E. Messina, A. Jacoff, Performance standards for urban search and rescue robots, in Unmanned Systems Technology VIII, vol. 6230 (International Society for Optics and Photonics, 2006), p. 62301VGoogle Scholar
  39. 39.
    J.B. Guinée, Handbook on life cycle assessment operational guide to the ISO standards. Int. J. Life Cycle Assess. 7(5), 311 (2002)CrossRefGoogle Scholar
  40. 40.
    M. Delvaux, Draft report with recommendations to the Commission on Civil Law Rules on Robotics, vol. 22. European Parliament: Brussels, Belgium (2016)Google Scholar
  41. 41.
    C.B. Frey, M.A. Osborne, The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tamás Haidegger
    • 1
    • 2
    Email author
  • Gurvinder S. Virk
    • 3
    • 4
  • Carol Herman
    • 5
  • Roger Bostelman
    • 6
  • Péter Galambos
    • 7
    • 8
  • György Györök
    • 7
    • 8
  • Imre J. Rudas
    • 7
    • 8
  1. 1.University Research, Innovation and Service Center, Óbuda UniversityBudapestHungary
  2. 2.Austrian Center for Medical Innovation and Technology (ACMIT)Wiener NeustadtAustria
  3. 3.CLAWAR Association LtdSheffieldUK
  4. 4.Innovative Technology & Science Ltd (InnoTecUK)CambridgeUK
  5. 5.Association for the Advancement of Medical InstrumentationArlingtonUSA
  6. 6.National Institute of Standards and TechnologyGaithersburgUSA
  7. 7.Antal Bejczy Center for Intelligent RoboticsÓbuda UniversityBudapestHungary
  8. 8.Antal Bejczy Center for Intelligent RoboticsÓbuda UniversityBudapest, SzékesfehérvárHungary

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