Advertisement

A Fabric-Based Approach for Softness Rendering

  • Matteo BianchiEmail author
  • Alessandro Serio
  • Enzo Pasquale Scilingo
  • Antonio Bicchi
Chapter
Part of the Springer Series on Touch and Haptic Systems book series (SSTHS)

Abstract

In this chapter we describe a softness display based on the contact area spread rate (CASR) paradigm. This device uses a stretchable fabric as a substrate that can be touched by users, while contact area is directly measured via an optical system. By varying the stretching state of the fabric, different stiffness values can be conveyed to users. We describe a first technological implementation of the display and compare its performance in rendering various levels of stiffness with the one exhibited by a pneumatic CASR-based device. Psychophysical experiments are reported and discussed. Afterwards, we present a new technological implementation for the fabric-based display, with reduced dimensions and faster actuation, which enables rapid changes in the fabric stretching state. These changes are mandatory to properly track typical force/area curves of real materials. System performance in mimicking force-area curves obtained from real objects exhibits a high degree of reliability, also in eliciting overall discriminable levels of softness.

Keywords

Contact Area Contact Force Haptic Device Linear Actuator Tactile Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the European Research Council under the ERC Advanced Grant \(n^\circ \) 291166 SoftHands (A Theory of Soft Synergies for a New Generation of Artificial Hands). The research leading to these results has also received funding from the European Union Seventh Framework Programme FP7/2007–2013 under grand agreement \(n^{\circ }\) 248587 THE (The Hand Embodied) and under grant agreement \(n^{\circ }\) 601165 WEARHAP (WEARable HAPtics for humans and robots).

References

  1. Adelson EH, Bergen JR (1991) The plenoptic function and the elements of early vision. Landy M, Movshon JA (eds) Computational models of visual processing. MIT Press, Cambridge, pp 3–20Google Scholar
  2. Bastian HC (1888) The ‘muscular sense’: its nature and cortical localisation. Brain 10:1–137CrossRefGoogle Scholar
  3. Bianchi M (2012) On the role of haptic synergies in modelling the sense of touch and in designing artificial haptic systems. PhD thesis, University of Pisa, Pisa, ItalyGoogle Scholar
  4. Bianchi M, Salaris P, Bicchi A (2013a) Synergy-based hand pose sensing: optimal glove design. Int J Robot Res 32(4):407–424CrossRefGoogle Scholar
  5. Bianchi M, Salaris P, Bicchi A (2013b) Synergy-based hand pose sensing: reconstruction enhancement. Int J Robot Res 32(4):396–406CrossRefGoogle Scholar
  6. Bianchi M, Scilingo EP, Serio A, Bicchi A (2009) A new softness display based on bi-elastic fabric. In: World haptics conference, pp 382–383Google Scholar
  7. Bianchi M, Serio A, Scilingo EP, Bicchi A (2010) A new fabric-based softness display. In: Proceedings of IEEE haptics symposium, pp 105–112Google Scholar
  8. Bicchi A, De Rossi DE, Scilingo EP (2000) The role of the contact area spread rate in haptic discrimination of softness. IEEE Trans Robot Autom 16(5):496–504CrossRefGoogle Scholar
  9. Bicchi A, Gabiccini M, Santello M (2011) Modelling natural and artificial hands with sinergie. Phil Trans R Soc B 366:3153–3161CrossRefGoogle Scholar
  10. Bicchi A, Scilingo EP, Dente D, Sgambelluri N (2005) Tactile flow and haptic discrimination of softness. In: Barbagli F, Prattichizzo D, Salisbury K (eds) Multi-point interaction with real and virtual objects, pp 165–176 (STAR: Springer tracts in advanced robotics)Google Scholar
  11. Bicchi A, Scilingo EP, Ricciardi E, Pietrini P (2008) Tactile flow explains haptic counterparts of common visual illusions. Brain Res Bull 75(6):737–741CrossRefGoogle Scholar
  12. Brown C, Asada H (2007) Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal component analysis. In: IEEE-RAS international conference on intelligent robots and systems, pp 2877–2882Google Scholar
  13. Catalano MG, Grioli G, Serio A, Farnioli E, Piazza C, Bicchi A (2012) Adaptive synergies for a humanoid robot hand. In: IEEE-RAS international conference on humanoid robots, pp 7–14Google Scholar
  14. Ciocarlie MT, Allen PK (2009) Hand posture subspaces for dexterous robotic grasping. Int J Robot Res 28(7):851–867CrossRefGoogle Scholar
  15. Ciocarlie MT, Goldfeder C, Allen PK (2007) Dimensionality reduction for hand-independent dexterous robotic grasping. In: IEEE/RSJ international conference on intelligent robots and systems, pp 3270–3275Google Scholar
  16. Dandekar K, Raju BI, Srinivasan MA (2003) 3-d finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. ASME J Biomech Eng 125:682–691CrossRefGoogle Scholar
  17. Friedman RM, Hetster KD, Green BG, LaMotte RH (2008) Magnitude estimation of softness. Exp Brain Res 191(2):133–142CrossRefGoogle Scholar
  18. Fujita K, Ohmori H (2001) A new softness display interface by dynamic fingertip contact area control. In: World multiconference on systemics, cybernetics and informatics, pp 78–82Google Scholar
  19. Grioli G, Bicchi A (2010) A non-invasive real-time method for measuring variable stiffness. In: Robotics science and systemsGoogle Scholar
  20. Hannaford B, Okamura AM (2008) Haptics. In: Siciliano B, Khatib O (eds) Springer handbook on robotics. Springer, Heidelberg, pp 719–739Google Scholar
  21. Hayward V (2011) Is there a “plenhaptic” function? Phil Trans R Soc B 366:3115–3122CrossRefGoogle Scholar
  22. Horn BKP, Schunk BG (1981) Determining optical flow. Artif Intell 17:185–203CrossRefGoogle Scholar
  23. Johnson KO (2001) The roles and functions of cutaneous mechanoreceptors. Curr Opin Neurobiol 11(4):455–461CrossRefGoogle Scholar
  24. Kern TA (2009) Biological basics of haptic perception. Kern TA (ed) Engineering haptic devices. Springer, Heidelberg, pp 35–58Google Scholar
  25. Klatzky RL, Lederman SJ, Matula DE (1991) Imagined haptic exploration in judgements of objects properties. J Exper Psychol Learn Mem Cogn 17(1):314–322CrossRefGoogle Scholar
  26. Klatzky RL, Lederman SJ, Reed C (1989) Haptic integration of object properties:texture, hardness, and planar contour. J Exper Psychol: Hum Percept Perform 15(1):45–57Google Scholar
  27. Latash ML (2008) Synergy. Oxford University Press, OxfordGoogle Scholar
  28. Lederman SJ, Klatzky RL (1987) Hand movements: a window into haptic object recognition. Cogn Psychol 19(12):342–368CrossRefGoogle Scholar
  29. Lederman SJ, Klatzky RL (1997a) Relative availability of surface and object properties during early haptic processing. J Exper Psychol: Hum Percept Perform 23(6):1680Google Scholar
  30. Lederman SL, Klatzky RL (1997b) Relative availability of surface and object properties during early haptic processing. J Exper Psychol: Hum Percept Perform 23(6):1680–1707Google Scholar
  31. Newman SD, Klatzky RL, Lederman SJ, Just MA (2005) Imagining material versus geometric properties of objects: an fMRI study. Cogn Brain Res 23(3):235–246CrossRefGoogle Scholar
  32. Santello M, Baud-Bovy G, Jörntell H (2013) Neural bases of hand synergies. Frontiers Comput Neurosci 7(23)Google Scholar
  33. Schieber MH, Santello M (2004) Hand function: peripheral and central constraints on performance. J Appl Physiol 96(6):2293–2300CrossRefGoogle Scholar
  34. Scilingo EP, Bianchi M, Grioli G, Bicchi A (2010) Rendering softness: integration of kinaesthetic and cutaneous information in a haptic device. IEEE Trans Haptics 3(2):109–118CrossRefGoogle Scholar
  35. Scilingo EP, Sgambelluri N, Tonietti G, Bicchi A (2007) Integrating two haptic devices for performance enhancement. In: EuroHaptics conference, 2007 and symposium on haptic interfaces for virtual environment and teleoperator systems. World haptics 2007. Second Joint, IEEE, pp 139–144Google Scholar
  36. Serio A, Bianchi M, Bicchi A (2013) A device for mimicking the contact force/contact area relationship of different materials with applications to softness rendering. In: IEEE/RSJ international conference on intelligent robots and systems, 2013, IROS 2013, pp 4484–4490Google Scholar
  37. Serio A, Grioli G, Sardellitti I, Tsagarakis NG, Bicchi A (2011) A decoupled impedance observer for a variable stiffness robot. In: 2011 IEEE international conference on robotics and automation, pp 5548–5553Google Scholar
  38. Srinivasan MA, LaMotte RH (1995) Tactile discrimination of softness. J Neurophysiol 73(1): 88–101Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Matteo Bianchi
    • 1
    • 2
    Email author
  • Alessandro Serio
    • 2
  • Enzo Pasquale Scilingo
    • 2
  • Antonio Bicchi
    • 1
    • 2
  1. 1.Department of Advanced Robotics (ADVR)Istituto Italiano di TecnologiaGenoaItaly
  2. 2.Research Centre “E. Piaggio”Università di PisaPisaItaly

Personalised recommendations