SMOOTH Robot: Design for a Novel Modular Welfare Robot

  • William K. JuelEmail author
  • Frederik Haarslev
  • Eduardo R. Ramírez
  • Emanuela Marchetti
  • Kerstin Fischer
  • Danish Shaikh
  • Poramate Manoonpong
  • Christian Hauch
  • Leon Bodenhagen
  • Norbert Krüger


Demographic change is expected to challenge many societies in the next few decades if todays’ standards of services in e.g. elder care shall be maintained. Robots are considered to at least partially mitigate this challenge, however, robots are rarely applied in the welfare domain. This paper describes the development of a concept for a novel welfare robot that is modular and affordable. The development is based on a participatory design process and by taking strengths and limitations of selected, commercially available robots into account. This work contributes a design methodology specific for welfare robots and a resulting robot concept that address three use cases in a care center. The concept includes multi-modal robot perception that facilitates a proactive robot behavior for achieving smooth interactions with end-users.


Robotics Welfare Healthcare Design HRI 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank the staff and residents at Ølby elderly care center for the fruitful discussions and valuable insights that have been shared. We would also like to thank Mobile Industrial Robots, Fraunhofer Institute, Softbanks Robotics and PAL Robotics for letting us use images of their robots.


  1. 1.
    Aethon: Tug., (accessed on 13/8/2018) (2014)
  2. 2.
    AR track Alvar: Ar track alvar., (accessed on 2019-07-06) (2013)
  3. 3.
    Bent Hansen, C.H.: Pres på sundhedsvæsenet derfor stiger sygehusudgifterne – sådan holder vi væksten nede (2015)Google Scholar
  4. 4.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  5. 5.
    Blue Ocean Robotics: Uv disinfection robot., (accessed on 13/8/2018) (2016)
  6. 6.
    Brandt, E, Binder, T, Sanders, E: Tools and Techniques: Ways to Engage Telling, Making and Enacting. Routledge International Handbooks (2012)Google Scholar
  7. 7.
    Cao, Z, Simon, T, Wei, S, Sheikh, Y: Realtime multi-person 2d pose estimation using part affinity fields. arXiv:1611.08050 (2016)
  8. 8.
    Castro-González, A, Admoni, H., Scassellati, B.: Effects of form and motion on judgments of social robots’ animacy, likability, trustworthiness and unpleasantness. Int. J. Human-Comput. Stud. 90, 27–38 (2016). CrossRefGoogle Scholar
  9. 9.
    Cherubini, A., Passama, R., Fraisse, P., Crosnier, A.: A unified multimodal control framework for human–robot interaction. Robot. Auton. Syst. 70, 106–115 (2015). CrossRefGoogle Scholar
  10. 10.
    Cohen, B.J., Chitta, S., Likhachev, M.: Search-based planning for manipulation with motion primitives. In: IEEE International Conference on Robotics and Automation. IEEE, Anchorage (2010)Google Scholar
  11. 11.
    Coradeschi, S, Cesta, A, Cortellessa, G, Coraci, L, Galindo, C, Gonzalez, J, Karlsson, L, Forsberg, A, Frennert, S, Furfari, F, Loutfi, A, Orlandini, A, Palumbo, F, Pecora, F, von Rump, S, Štimec, A, Ullberg, J, Ötslund, B: GiraffPlus: A System for Monitoring Activities and Physiological Parameters and Promoting Social Interaction for Elderly, pp 261–271. Springer International Publishing, Cham (2014)Google Scholar
  12. 12.
    Dereshev, D., Kirk, D.: Form, function and etiquette–potential users’ perspectives on social domestic robots. Multimodal Technol. Interact. 1, 2 (2017). CrossRefGoogle Scholar
  13. 13.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Fischinger, D, Einramhof, P, Papoutsakis, K, Wohlkinger, W, Mayer, P, Panek, P, Hofmann, S, Koertner, T, Weiss, A, Argyros, A, Vincze, M: Hobbit, a care robot supporting independent living at home: First prototype and lessons learned. Robot. Auton. Syst. 75, 60–78 (2016)., assistance and Service Robotics in a Human EnvironmentCrossRefGoogle Scholar
  15. 15.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte Carlo localization: Efficient position estimation for mobile robots. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, Orlando (1999)Google Scholar
  16. 16.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)CrossRefGoogle Scholar
  17. 17.
    Haarslev, F., Docherty, D., Suvei, S.D., Juel, W.K., Bodenhagen, L., Shaikh, D., Krüger, N, Manoonpong, P.: Towards crossmodal learning for smooth multimodal attention orientation. In: Social Robotics, pp 318–328. Springer International Publishing, Cham (2018)CrossRefGoogle Scholar
  18. 18.
    Hashimoto, K., Saito, F., Yamamoto, T., Ikeda, K: A field study of the human support robot in the home environment (2013)Google Scholar
  19. 19.
    He, K, Gkioxari, G, Dollár, P, Girshick, RB: Mask r-cnn. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)Google Scholar
  20. 20.
    He, W, Goodkind, D, Kowal, P: An aging world: 2015 international population reports. US Census Bureau (2016)Google Scholar
  21. 21.
    Hess, W., Kohler, D., Rapp, H., Andor, D: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp 138–143. IEEE, Stockholm (2016)Google Scholar
  22. 22.
    Howard, A.G., Zhu, M, Chen, B, Kalenichenko, D, Wang, W, Weyand, T, Andreetto, M, Adam, H: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)
  23. 23.
    Juel, W, Krüger, N, Bodenhagen, L: Robots for elderly care institutions: How they may affect elderly care. Frontiers in Artificial Intelligence and Applications (2018)Google Scholar
  24. 24.
    Kittmann, R., Frölich, T, Schäfer, J, Reiser, U, Weisshardt, F, Haug, A: Let me introduce myself: I am care-o-bot 4, a gentleman robot. Mensch und Computer 2015 Tagungsband (2015)Google Scholar
  25. 25.
    Lemaignan, S, Warnier, M, Sisbot, E, Alami, R: Human-robot interaction: Tackling the ai challenges. Artificial Intelligence – Special Issue on Robotics (2015)Google Scholar
  26. 26.
    Lin, T, Maire, M, Belongie, S.J., Bourdev, L.D., Girshick, R.B., Hays, J, Perona, P, Ramanan, D, Dollár, P, Zitnick, CL: Microsoft COCO: Common objects in context. arXiv:1405.0312(2014)
  27. 27.
    Lu, D.V., Hershberger, D, Smart, W.D.: Layered costmaps for context-sensitive navigation. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 709–715 (2014)Google Scholar
  28. 28.
    Mobile Industrial Robots: Mir100., (accessed on 13/8/2018) (2013)
  29. 29.
    Mobile Mask R-CNN: Mobile mask r-cnn., (accessed on 2019-07-06) (2018)
  30. 30.
    Mutlu, B, Forlizzi, J: Robots in organizations: The role of workflow, social, and environmental factors in human-robot interaction. In: HRI 2008 - Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot Interaction: Living with Robots, pp. 287–294 (2008)Google Scholar
  31. 31.
    PAL Robotics: TIAGo., (accessed on 13/8/2018) (2015)
  32. 32.
    Pink, S: Doing Visual Ethnography. Sage (2001)Google Scholar
  33. 33.
    Porr, B, Wörgötter, F: Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neur Comput 18(6), 1380–1412 (2006)CrossRefGoogle Scholar
  34. 34.
    Preece, Y.J., Rogers, S.H.: Interaction Design: Beyond Human-Computer Interaction. Wiley (2015)Google Scholar
  35. 35.
    Rehrl, T., Geiger, J., Golcar, M., Gentsch, S., Knobloch, J., Rigoll, G., Scheibl, K., Schneider, W., Ihsen, S., Wallhoff, F.: The robot alias as a database for health monitoring for elderly people. In: Wichert, R, Klausing, H (eds.) Ambient Assisted Living, pp 225–245. Springer, Berlin (2014)Google Scholar
  36. 36.
    Riek, L.D.: Healthcare robotics. Commun. ACM 60(11), 68–78 (2017). CrossRefGoogle Scholar
  37. 37.
    ROS Navigation Stack: Ros navigation stack., (accessed on 2019-27-05) (2013)
  38. 38.
    Rösmann, C, Feiten, W., Wösch, T, Hoffmann, F., Bertram, T: Efficient trajectory optimization using a sparse model. In: Proc. IEEE European Conference on Mobile Robots, pp 138–143. IEEE, Barcelona (2013)Google Scholar
  39. 39.
    Barn, R.: Rubens barn., (accessed on 13/8/2018) (2018)
  40. 40.
    Schroff, F, Kalenichenko, D, Philbin, J: Facenet: A unified embedding for face recognition and clustering. arXiv:1503.03832 (2015)
  41. 41.
    Shaikh, D, Bodenhagen, L, Manoonpong, P: Concurrent intramodal learning enhances multisensory responses of symmetric crossmodal learning in robotic audio-visual tracking. Cognitive Systems Research 54, 138–153 (2019). CrossRefGoogle Scholar
  42. 42.
    SMOOTH: Seamless human-robot interaction for the support of elderly people., (accessed on 13/8/2018) (2017)
  43. 43.
    Softbank Robotics: Nao, romeo and pepper., (accessed on 13/8/2018) (2014)
  44. 44.
    Spinuzzi, C.: The methodology of participatory design. Tech. Commun. 52(2), 163–174 (2005)Google Scholar
  45. 45.
    Ventura, J., Bichard, J.A.: Design anthropology or anthropological design? towards ’social design’. Int. J. Des. Creativ. Innov. 5(3–4), 222–234 (2017). CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • William K. Juel
    • 1
    Email author
  • Frederik Haarslev
    • 1
  • Eduardo R. Ramírez
    • 1
  • Emanuela Marchetti
    • 2
  • Kerstin Fischer
    • 3
  • Danish Shaikh
    • 4
  • Poramate Manoonpong
    • 4
  • Christian Hauch
    • 5
  • Leon Bodenhagen
    • 1
  • Norbert Krüger
    • 1
  1. 1.SDU Robotics, Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdenseDenmark
  2. 2.Department for the Study of CultureUniversity of Southern DenmarkOdenseDenmark
  3. 3.Department of Design and CommunicationUniversity of Southern DenmarkSønderborgDenmark
  4. 4.SDU Embodied Systems for Robotics and Learning, Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdenseDenmark
  5. 5.Robotize ApsKgs. LyngbyDenmark

Personalised recommendations