Skip to main content

A Fabric-Based Approach for Softness Rendering

  • Chapter
  • First Online:
Multisensory Softness

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 3–20

    Google Scholar 

  • Bastian HC (1888) The ‘muscular sense’: its nature and cortical localisation. Brain 10:1–137

    Article  Google Scholar 

  • 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, Italy

    Google Scholar 

  • Bianchi M, Salaris P, Bicchi A (2013a) Synergy-based hand pose sensing: optimal glove design. Int J Robot Res 32(4):407–424

    Article  Google Scholar 

  • Bianchi M, Salaris P, Bicchi A (2013b) Synergy-based hand pose sensing: reconstruction enhancement. Int J Robot Res 32(4):396–406

    Article  Google Scholar 

  • Bianchi M, Scilingo EP, Serio A, Bicchi A (2009) A new softness display based on bi-elastic fabric. In: World haptics conference, pp 382–383

    Google Scholar 

  • Bianchi M, Serio A, Scilingo EP, Bicchi A (2010) A new fabric-based softness display. In: Proceedings of IEEE haptics symposium, pp 105–112

    Google Scholar 

  • 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):496–504

    Article  Google Scholar 

  • Bicchi A, Gabiccini M, Santello M (2011) Modelling natural and artificial hands with sinergie. Phil Trans R Soc B 366:3153–3161

    Article  Google Scholar 

  • 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 

  • Bicchi A, Scilingo EP, Ricciardi E, Pietrini P (2008) Tactile flow explains haptic counterparts of common visual illusions. Brain Res Bull 75(6):737–741

    Article  Google Scholar 

  • 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 2877–2882

    Google Scholar 

  • 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 7–14

    Google Scholar 

  • Ciocarlie MT, Allen PK (2009) Hand posture subspaces for dexterous robotic grasping. Int J Robot Res 28(7):851–867

    Article  Google Scholar 

  • 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 3270–3275

    Google Scholar 

  • 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 125:682–691

    Article  Google Scholar 

  • Friedman RM, Hetster KD, Green BG, LaMotte RH (2008) Magnitude estimation of softness. Exp Brain Res 191(2):133–142

    Article  Google Scholar 

  • 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 78–82

    Google Scholar 

  • Grioli G, Bicchi A (2010) A non-invasive real-time method for measuring variable stiffness. In: Robotics science and systems

    Google Scholar 

  • Hannaford B, Okamura AM (2008) Haptics. In: Siciliano B, Khatib O (eds) Springer handbook on robotics. Springer, Heidelberg, pp 719–739

    Google Scholar 

  • Hayward V (2011) Is there a “plenhaptic” function? Phil Trans R Soc B 366:3115–3122

    Article  Google Scholar 

  • Horn BKP, Schunk BG (1981) Determining optical flow. Artif Intell 17:185–203

    Article  Google Scholar 

  • Johnson KO (2001) The roles and functions of cutaneous mechanoreceptors. Curr Opin Neurobiol 11(4):455–461

    Article  Google Scholar 

  • Kern TA (2009) Biological basics of haptic perception. Kern TA (ed) Engineering haptic devices. Springer, Heidelberg, pp 35–58

    Google Scholar 

  • Klatzky RL, Lederman SJ, Matula DE (1991) Imagined haptic exploration in judgements of objects properties. J Exper Psychol Learn Mem Cogn 17(1):314–322

    Article  Google Scholar 

  • 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):45–57

    Google Scholar 

  • Latash ML (2008) Synergy. Oxford University Press, Oxford

    Google Scholar 

  • Lederman SJ, Klatzky RL (1987) Hand movements: a window into haptic object recognition. Cogn Psychol 19(12):342–368

    Article  Google Scholar 

  • Lederman SJ, Klatzky RL (1997a) Relative availability of surface and object properties during early haptic processing. J Exper Psychol: Hum Percept Perform 23(6):1680

    Google Scholar 

  • Lederman SL, Klatzky RL (1997b) Relative availability of surface and object properties during early haptic processing. J Exper Psychol: Hum Percept Perform 23(6):1680–1707

    Google Scholar 

  • Newman SD, Klatzky RL, Lederman SJ, Just MA (2005) Imagining material versus geometric properties of objects: an fMRI study. Cogn Brain Res 23(3):235–246

    Article  Google Scholar 

  • Santello M, Baud-Bovy G, Jörntell H (2013) Neural bases of hand synergies. Frontiers Comput Neurosci 7(23)

    Google Scholar 

  • Schieber MH, Santello M (2004) Hand function: peripheral and central constraints on performance. J Appl Physiol 96(6):2293–2300

    Article  Google Scholar 

  • 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):109–118

    Article  Google Scholar 

  • 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 139–144

    Google Scholar 

  • 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 4484–4490

    Google Scholar 

  • 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 5548–5553

    Google Scholar 

  • Srinivasan MA, LaMotte RH (1995) Tactile discrimination of softness. J Neurophysiol 73(1): 88–101

    Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matteo Bianchi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag London

About this chapter

Cite this chapter

Bianchi, M., Serio, A., Scilingo, E.P., Bicchi, A. (2014). A Fabric-Based Approach for Softness Rendering. In: Di Luca, M. (eds) Multisensory Softness. Springer Series on Touch and Haptic Systems. Springer, London. https://doi.org/10.1007/978-1-4471-6533-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6533-0_11

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6532-3

  • Online ISBN: 978-1-4471-6533-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics