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Towards a Truly Cooperative Guidance and Control: Generic Architecture for Intuitive Human-Machine Cooperation

  • Marcel UsaiEmail author
  • Ronald Meyer
  • Hiroshi Nagahara
  • Yusaku Takeda
  • Frank Flemisch
Conference paper
  • 8 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

Human-machine cooperation (HMC) is often still rigid and unintuitive. However, with more ability transferred to machines, the need for intuitive cooperation rises. To achieve this, new concepts need to arise and be implemented for machines to get a better understanding of their cooperation partner and to be able to act as expected. This includes adapted cooperation schemes based on actual dimension of control, e.g. conscious or subconscious HMC. In this paper, we give an overview on a generic architecture designed to achieve intuitive HMC and introduction to an example application.

Keywords

Human-machine cooperation Shared control Intuitive control Cooperative guidance 

References

  1. 1.
    Nowak, M.A.: Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006)CrossRefGoogle Scholar
  2. 2.
    Flemisch, F., Abbink, D.A., Itoh, M., Pacaux-Lemoine, M.-P., Weßel, G.: Joining the blunt and the pointy end of the spear: towards a common framework of joint action, human–machine cooperation, cooperative guidance and control, shared, traded and supervisory control. Cogn. Technol. Work 21(4), 555–568 (2019).  https://doi.org/10.1007/s10111-019-00576-1CrossRefGoogle Scholar
  3. 3.
    Flemisch, F., Adams, C.A., Conway, S.R., Goodrich, K.H., Palmer, M.T., Schutte, P.C.: The H-Metaphor as a guideline for vehicle automation and interaction; NASA/TM—2003-212672 (2003)Google Scholar
  4. 4.
    Hoc, J.-M.: From human-machine interaction to human-machine cooperation. Ergonomics 43(7), 833–843 (2000)CrossRefGoogle Scholar
  5. 5.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE SMC – Part A Syst. Hum. 30(3), 286–297 (2000)Google Scholar
  6. 6.
    Flemisch, F., Altendorf, E., Canpolat, Y., Weßel, G., Baltzer, M., Lopez, D., Voß, G. M. I., Schwalm, M., Schutte, P.: Uncanny and unsafe valley of assistance and automation: first sketch and application to vehicle automation. In: Advances in Ergonomic Design of Systems, Products and Processes, pp. 319–334 (2017)Google Scholar
  7. 7.
    Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models; IEEE-SMC, May 1983Google Scholar
  8. 8.
    Flemisch, F., Schieben, A., Kelsch, J., Löper, C.: Automation spectrum, inner/ outer compatibility and other potentially useful human factors concepts for assistance and automation. Human Factors for Assistance and Automation (2008)Google Scholar
  9. 9.
    Flemisch, F., Schutte, P.: Fluidity in control distributions of automation. NASA Langley 2003Google Scholar
  10. 10.
    Allain, M., Konduri, S., Maske, H., Pagilla, P.R., Chowdhary, G.: Blended shared control of a hydraulic excavator. IFAC-PapersOnLine 50(1), 14928–14933 (2017)CrossRefGoogle Scholar
  11. 11.
    Baltzer, M., Flemisch, F., Altendorf, E., Meier, S.: Mediating the interaction between human and automation during the arbitration processes in cooperative guidance and control of highly automated vehicles. In: Proceedings of the 5th International Conference on AHFE, pp. 2107–2118 (2014)Google Scholar
  12. 12.
    Bennett, D.J.: Stretch reflex responses in the human elbow joint during a voluntary movement. J. Physiol. 474(2), 339–351 (1994)CrossRefGoogle Scholar
  13. 13.
    Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M.: The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414, 446–449 (2001)CrossRefGoogle Scholar
  14. 14.
    Shadmehr, R., Mussa-Ivaldi, F.A.: Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994)CrossRefGoogle Scholar
  15. 15.
    Kikuuwe, R., Yamamoto, T., Fujimoto, H.: A guideline for low-force robotic guidance for enhancing human performance of positioning and trajectory tracking: it should be stiff and appropriately slow. IEEE SMC - Part A Syst. Hum. 38(4), 945–957 (2008)CrossRefGoogle Scholar
  16. 16.
    Domen, K., Latash, M.L., Zatsiorsky, V.M.: Reconstruction of equilibruim trajectories during whole-body movements. Biol. Cybern. 80, 195–204 (1999)CrossRefGoogle Scholar
  17. 17.
    Ishio, J., Ichikawa, H., Kano, Y., Abe, M.: Vehicle-handling quality evaluation through model-based driver steering behavior. Veh. Syst. Dyn. 46, 549–560 (2008)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marcel Usai
    • 1
    Email author
  • Ronald Meyer
    • 1
  • Hiroshi Nagahara
    • 2
  • Yusaku Takeda
    • 2
  • Frank Flemisch
    • 1
    • 3
  1. 1.IAW of RWTH Aachen UniversityAachenGermany
  2. 2.Mazda Motor Corporation, Technical Research CenterHiroshimaJapan
  3. 3.Fraunhofer Institute for Communication, Information, Processing and Ergonomics (FKIE)WachtbergGermany

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