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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 95))

Abstract

We have developed a finger-shaped sensor array (BioTacĀ®) that provides simultaneous information about contact forces, microvibrations and thermal fluxes, mimicking the full cutaneous sensory capabilities of the human finger. For many tasks, such as identifying objects or maintaining stable grasp, these sensory modalities are synergistic. For example, information about the material composition of an object can be inferred from the rate of heat transfer from a heated finger to the object, but only if the location and force of contact are well controlled. In this chapter we introduce the three sensing modalities of our sensor and consider how they can be used synergistically. Tactile sensing and signal processing is necessary for human dexterity and is likely to be required in mechatronic systems such as robotic and prosthetic limbs if they are to achieve similar dexterity.

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References

  1. J.C. Rothwell, M.M. Traub, B.L. Day, J.A. Obesko, P.K. Thomas, C.D. Marsden, Manual motor performance in a deafferenated man. Brain 105, 515ā€“542 (1982)

    ArticleĀ  Google ScholarĀ 

  2. G. Westling, R.S. Johansson, Factors influencing the force control during precision grip. Exp. Brain Res. 53(2), 277ā€“284 (1984)

    ArticleĀ  Google ScholarĀ 

  3. R.D. Howe, Tactile sensing and control of robotic manipulation. J. Adv. Robot. 8(3), 245ā€“261 (1994)

    ArticleĀ  Google ScholarĀ 

  4. M.H. Lee, H.R. Nichols, Tactile sensing for mechatronicsā€”a state of the art survey. Mechatronics 9, 1ā€“31 (1999)

    ArticleĀ  Google ScholarĀ 

  5. C. Melchiorri, Tactile sensing for robotic manipulation. Ramsete: Lecture Notes in Control and Information Sciences, vol. 270 (Springer, Berlin, 2001)

    Google ScholarĀ 

  6. R.S. Dahiya, G. Metta, M. Valle, G. Sandini, Tactile sensingā€”from humans to humanoids. IEEE Trans. Rob. 26(1), 1ā€“20 (2010)

    ArticleĀ  Google ScholarĀ 

  7. N. Wettels, V. J. Santos, R. S. Johansson, G. E. Loeb, Biomimetic tactile sensor array. Adv. Rob. 22(7), 829ā€“849 (2008)

    Google ScholarĀ 

  8. J. Fishel, V. J. Santos, G. E. Loeb, A robust microvibration sensor for biomimetic fingertips, in Proceedings IEEE International Conference on Biomedical Robotics and Biomechatronics, Scottsdale, AZ, pp. 659ā€“663, 2008

    Google ScholarĀ 

  9. D. Roy, N. Wettels, G. E. Loeb, Elastomeric skin selection for a fluid-filled artificial fingertip. J. Appl. Polym. Sci., in press (online 7 June 2012)

    Google ScholarĀ 

  10. C. H. Lin, T. W. Erickson, J. A. Fishel, N. Wettels, G. E. Loeb, Signal processing and fabrication of a biomimetic tactile sensor array with thermal, force and microvibration modalities, in IEEE International Conference on Robotics and Biomimetics, 129ā€“134, 2009

    Google ScholarĀ 

  11. C.M. Bishop, Neural Networks for Pattern Recognition (University Press, Oxford, 1995)

    Google ScholarĀ 

  12. N. Wettels, Biomimetic tactile sensor for object identification and grip control, Dissertation, University of Southern California, May 2011

    Google ScholarĀ 

  13. M.T. Hagan, M. Menhaj, Training multilayer networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5, 989ā€“993 (1994)

    ArticleĀ  Google ScholarĀ 

  14. W. S. Sarle, Stopped training and other remedies for overfitting, in Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, pp. 352ā€“360, 1995

    Google ScholarĀ 

  15. K.T. Lau, W. Guo, B.M. Kiernan, C. Slater, D. Diamond, Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization. Sens. Actuators B 1(2), 242ā€“247 (2009)

    ArticleĀ  Google ScholarĀ 

  16. G. McLachlan, D. Peel, Finite Mixture Models (Wiley, New York, 2000)

    Google ScholarĀ 

  17. S. Calinon, Robot Programming by Demonstration: A Probabilistic Approach (EPFL/CRC Press, Lausanne, 2009)

    Google ScholarĀ 

  18. V.B. Mountcastle, R.H. LaMotte, G. Carli, Detection thresholds for stimuli in humans and monkeys: comparison with threshold events in mechanoreceptive afferent nerve fibers innervating the monkey hand. J. Neurophysiol. 35, 122ā€“136 (1972)

    Google ScholarĀ 

  19. R.S. Johansson, U. Landstrom, R. Lundstrom, Responses of mechanoreceptive afferent units in glabrous skin of the human hand to sinusoidal skin displacements. Brain Res. 244(1), 17ā€“25 (1982)

    ArticleĀ  Google ScholarĀ 

  20. A. Pruski, B. Mutel, Direct contact sensors based on carbon fibre, in IEEE international conference on Robotics and Factories of the Future, pp. 409ā€“415, 1984

    Google ScholarĀ 

  21. J.A. Fishel, G.E. Loeb, Bayesian exploration for intelligent identification of textures. Front. Neurorobotics 6(4), 1ā€“20 (2012)

    Google ScholarĀ 

  22. R. D. Howe, M. R. Cutkosky, Sensing skin acceleration for texture and slip perception, in Proceedings IEEE International Conference on Robotics and Automation, Scottsdale, vol. 1, pp. 145ā€“150, 1989

    Google ScholarĀ 

  23. S. Selvarasah et al., A Three-dimensional thermal sensor based on single walled carbon nanotubes, in 14th International Conference on Solid-State Sensors, Actuators and Microsystems, pp. 1023ā€“1026, Lyon, June 2007

    Google ScholarĀ 

  24. Y. J. Yang et al., A wireless flexible temperature and tactile sensing array for robot applications in Proceedings Of Fourth International Symposium on Precision Mechanical Measurements, Dec 2008

    Google ScholarĀ 

  25. T. Someya et al., Conformable, flexible, large-area networks of pressure and thermal sensors with organic transistor active matrixes. PNAS 102(35), 2321ā€“12325 (2005)

    ArticleĀ  Google ScholarĀ 

  26. K. Shida, J. Yuji, Thermal-type tactile sensor for material discrimination and contact pressure sensing, in Proceedings of 41st Annual SICE Conference, vol. 1, pp. 588ā€“589, Aug 2002

    Google ScholarĀ 

  27. A. Persichetti, F. Vecchi, M. C. Carrozza, Optoelectronic-based flexible contact sensor for prosthetic hand application, in IEEE Conference on Rehabilitation Robotics, Netherlands, pp. 415ā€“420, 2007

    Google ScholarĀ 

  28. D.C. Spray, Cutaneous temperature receptors. Ann. Rev. Physiol. 48, 625ā€“638 (1986)

    ArticleĀ  Google ScholarĀ 

  29. E. Marin, The Role of Thermal Effusivity. Phys. Teach. 44, 432ā€“434 (2006)

    Google ScholarĀ 

  30. L. A. Jones, Perception and control of finger forces, in Proceedings of ASME Dynamic Systems and Control Division, pp. 133ā€“137, 1998

    Google ScholarĀ 

  31. S. Allin, Y. Matsuoka, R. Klatzky, Measuring just noticeable differences for haptic force feedback: Implications for rehabilitation, in Proceedings 10th Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, pp. 299ā€“302, 2002

    Google ScholarĀ 

  32. J. Scheibert, S. Leurent, A. Prevost, The role of fingerprints in the coding of tactile information probed with a biomimetic sensor. Science, 323(5920), 1503ā€“1506 (2009)

    Google ScholarĀ 

  33. L. A. Jones, M. Berris, Material discrimination and thermal perception, in Proceedings of the 11th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 137ā€“142, 2003

    Google ScholarĀ 

  34. Z. Su, J. A. Fishel, T. Yamamoto, G. E. Loeb, Use of tactile feedback to control exploratory movements to characterize object compliance. Front. Neurorobotics 6, 7 (2012). (doi: 10.3389/fnbot.2012.00007)

    Google ScholarĀ 

  35. N. Wettels, G. E. Loeb, Haptic feature extraction from a biomimetic tactile sensor: force, contact location and curvature, in Proceedings of IEEE/RAS International Conference on Robotics and Biomimetics, Phuket Thailand, 2011

    Google ScholarĀ 

  36. N. Wettels, J. A. Fishel, Z. Su, C. H. Lin, G. E. Loeb, Multi-modal synergistic tactile sensing. tactile sensing in humanoidsā€”tactile sensors and beyond workshop, in 9th IEEE/RAS International Conference on Humanoid Robots, Paris, 2009

    Google ScholarĀ 

  37. http://www.pressureprofile.com/products-robotouch

  38. R.S. Fearing, Tactile sensing mechanisms. Int. J. Robot. Res. 9(3), 3ā€“23 (1990)

    ArticleĀ  Google ScholarĀ 

  39. N. Futai, K. Matsumoto, I. Shimoyama, A flexible micromachined planar spiral inductor for use as an artificial tactile mechanoreceptor. Sens. Actuators, A 111(2ā€“3), 293ā€“303 (2004)

    ArticleĀ  Google ScholarĀ 

  40. http://www.inaba-rubber.co.jp/en/b_products/inastomer/index.html

  41. Z. Pan, Z. Zhu, Flexible full-body tactile sensor of low cost and minimal output connections for service robot. Ind. Robot Int. J. 32(6), 485ā€“491 (2005)

    Google ScholarĀ 

  42. http://www.peratech.com/qtctechnology.php

  43. http://www.ati-ia.com/products/ft/sensors.aspx

  44. http://www.tekscan.com/flexiforce.html

  45. L. Beccai et al., Design and fabrication of a hybrid silicon three-axial force sensor for biomechanical applications. Sens. Actuators A 120, 370ā€“382 (2005)

    ArticleĀ  Google ScholarĀ 

  46. Y.-L. Park, C. Majidi, R. Kramer, P. Berard, R. J. Wood, Hyperelastic pressure sensing with a liquid-embedded elastomer. J. Micromech. Microeng. 20(12), 125029 (2010)

    Google ScholarĀ 

  47. J. Engel et al, Flexible multimodal tactile sensing system for object identification, in Proceedings of IEEE EXCO SENSORS, Daegu, Oct 2006

    Google ScholarĀ 

  48. P. Dario, D. De Rossi, C. Domenici, R. Francesconi, Ferroelectric polymer tactile sensors with anthropomorphic features, in Proceedings IEEE International Conference on Robotics and Automation. Washington DC, vol. 1, pp. 332ā€“340, 1984

    Google ScholarĀ 

  49. R.D. Howe, M.R. Cutkosky, Dynamic tactile sensing: perception of fine surface features with stress rate sensing. IEEE Transact. Robot. Autom. 9(2), 140ā€“151 (1993)

    ArticleĀ  Google ScholarĀ 

  50. D. Hristu, N. Ferrier, R. W. Brockett, The performance of a deformable-membrane tactile sensor: basic results on geometrically-defined tasks, in Proceedings IEEE International Conference on Robotics and Automation, San Francisco, vol. 1, pp. 508ā€“513, 2000

    Google ScholarĀ 

  51. M. Ohka, Optical Three-axis tactile sensor, Mobile Robots: Perception & Navigation, InTech, 111ā€“136 (2007)

    Google ScholarĀ 

  52. E. E. Mitchell, R. DeMoyer, J. Vranish, A new MetGlas sensor. IEEE Trans. Industr. Electron. IE-33(2), 166ā€“170 (1986)

    Google ScholarĀ 

  53. J. M. Vranish, in Magnetoresistive Skin for Robots, ed. by A. Pugh. Robot Sensors, vol. 2: Tactile and Non-Vision (IFS Publications/Springer, New York, 1986), pp. 99ā€“111

    Google ScholarĀ 

  54. B. L. Hutchings, A. R. Grahn, R. J. Petersen, Multiple-layer cross field ultrasonic tactile sensor, in Proceedings IEEE International Conference on Robotics and Automation, vol. 3, pp. 2522ā€“2528, 1996

    Google ScholarĀ 

  55. A. R. Grahn, L. Astle, Robotic Ultrasonic Force Sensor Arrays, ed. by A. Pugh. Robot Sensors, vol. 2: Tactile and Non-Vision (IFS Publications/Springer, New York, 1986), pp. 297ā€“315

    Google ScholarĀ 

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 0912260 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Wettels, N., Fishel, J.A., Loeb, G.E. (2014). Multimodal Tactile Sensor. In: Balasubramanian, R., Santos, V. (eds) The Human Hand as an Inspiration for Robot Hand Development. Springer Tracts in Advanced Robotics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-03017-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-03017-3_19

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