Advertisement

Enhancing the Virtual Training Tool

Introducing Artificial Touch and Smell
  • Gaetano Canepa
Part of the Defense Research Series book series (DRSS, volume 6)

Abstract

This paper focuses on some of the possible applications of smell and touch to virtual reality training tools. In the first part the author will specify the characteristics and the problems of realizing artificial smell and touch systems. After, he will introduce some studies where he or the research centers where he works are directly involved. Most of the studies are just at the beginning.

Keywords

Virtual Reality Virtual Environment Tactile Sensor Virtual Patient Training Tool 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bergamasco, B. Allotta, L. Bosio, L. Ferretti, G. Parrini, G. M. Prisco, F. Salsedo, and G. Sartini. An arm exoskeleton system for teleoperation and virtual environments applications. In Proc. IEEE Int. Conf. on Robotics and Automation, pages 1449–1454, San Diego, California, 1994.Google Scholar
  2. Bicchi and P. Dario. Intrinsic tactile sensing for artificial hand. In R. Bolles and B. Roth, editors, Robotics Research, pages 83–90. MIT Press, Cambridge, MA, 1989.Google Scholar
  3. Burdea, J. Zhuang, E. Roskos, D. Silver, and N. Langrana. A portable dextrous master with force feedback. Presence: Teleoperators and Virtual Environments, I (1), 1992.Google Scholar
  4. Caiti, G. Canepa, D. De Rossi, F. Germagnoli, G. Magenes, and T. Parisini. Towards the realization of an artificial tactile system: Fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms. in print.Google Scholar
  5. Gaetano Canepa, Matteo Campanella, and Danilo De Rossi. Slip detection by a tactile neural network. In Proc. IEEE Int. Conf. on IROS, volume 1, pages 224–231, Munich, Germany, September 1994.Google Scholar
  6. Gaetano Canepa, Maurizio Morabito, Danilo De Rossi, Andrea Caiti, and Thomas Parisini. Shape estimation with tactile sensor: a radial basis function approach. In Proc. IEEE Int. Conf. on Decision and Control, Tucson, Arizona, 1992.Google Scholar
  7. Chiarelli and D. De Rossi. Modelling and mechanical characterization of thin fibers of contractile polymer hydro-gels. J. of Intell. Mater. Syst. and Struct., 3: 396–417, July 1992.CrossRefGoogle Scholar
  8. Chiarelli, D. De Rossi, A. Della Santa, and A. Mazzoldi. Doping induced volume change in a p-conjugated conducting polymer. Polymer Gels and Networks, 2: 289–297, 1994.CrossRefGoogle Scholar
  9. Chiarelli, K. Umezawa, and D. De Rossi. A polymer composite showing electrocontractile response. In D. De Rossi, K. Kajiwara, Y. Osada, and A. Yamauchi, editors, Polymers Gels: Fundamentals and Biomedical Applications, pages 195–204. Plenium Press, New York, 1991.CrossRefGoogle Scholar
  10. Danilo De Rossi, Claudio Domenici, Piero Chiarelli, and Gaetano Canepa. Biomimetic approaches to the design of materials for artificial haptics and manipulation. In Proc. of MEDICON ‘82, pages 689–692, Capri, Italy, 1992.Google Scholar
  11. Durlach and A. S. Mayor, editors. Virtual Reality: Scientific and Technological Challenges. National Academy Press, Washington, D.C., 1994.Google Scholar
  12. Gestri and A. Starita. A neural network for odour recognition. In The Fifth Int. Meeting on Chemical Sensors, pages 1090–1093, Roma, Italy, July 1992.Google Scholar
  13. Green, J. H. Hill, and R. M. Satava. Telepresence: Dextrous procedures in a virtual operating field. Surg. Endosc., 57: 192, 1991. Leon Harmon. Automated tactile sensing. The Int. J. of Robotic Research, 1(2), 1982.Google Scholar
  14. Hollerbach, I. W. Hunter, and J. Ballantyne. A comparative analysis of actuator technologies for robotics. In O. Khatib, J. J. Craig, and T. Lozano Perez, editors, The Robotics Review 2, pages 299–342. MIT Press, Cambridge, Mass., 1992.Google Scholar
  15. Hunter, T. D. Doukoglu, S. R. Lafontaine, P. G. Charrette, L. A. Jones, M. A. Sagar, G. D. Mallison, and P. J. Hunter. A teleoperated microsurgical robot and associated virtual environment for eye surgery. Presence, 2 (4): 265–280, 1994.Google Scholar
  16. Hunter, S. Lafontaine, P. M. F. Nielsen, R. J. Hunter, and J. M. Hollerbach. Manipulation and dynamic mechanical testing of microscopic objects using a tele-micro-robot system. IEEE Control System Magazine, I 0 (2): 3–9, 1990.Google Scholar
  17. Hurteau, S. DeSanctis, E. Begin, and M. Gagner. Laparoscopic surgery assisted by a robotic cameraman: Concept and experimental results. In Proc. IEEE Int. Conf. on Robotics and Automation, pages 2286–2289, San Diego, California, 1994.Google Scholar
  18. Jones and I. W. Hunter. Influence of the mechanical properties of a manipulandum on human operators dynamics:. elastic stiffness. Biological Cybernetics, 62: 299–307, 1990.CrossRefGoogle Scholar
  19. Jones and I. W. Hunter. Human operator perception of mechanical variables and their effects on tracking performance. Advances in Robotics, 42: 49–53, 1992.Google Scholar
  20. Kearney and I. W. Hunter. System identification of human joint dynamics. Critical Review in Biomedical Engineering, 18 (l): 55–87, 1987.Google Scholar
  21. Nannini and G. Serra. Growth of polypyrrole in a pattern: a technological approach to conducting polymers. J. of Molecular Electronics, 6: 124–128, 1990.Google Scholar
  22. Persaud and P. Pelosi. Sensor arrays using conducting polymers for an artificial nose. In J. W. Gardner and P. N. Bartlett, editors, Sensor and Sensory System for an Electronic Nose, NATO ASI Series, Series E: Appl. Sciences, pages 212–237. 1992.CrossRefGoogle Scholar
  23. Pope. Smell of success is in the air in effort to emulate the nose. Wall Street Journal, March 2, 1992.Google Scholar
  24. Ramoni. Ignorant influence diagrams. In M. Kaufmann, editor, Proc. of the Int. Joint Conf. on Artificial Intelligence, San Mateo, California, 1995.Google Scholar
  25. Rohling and J. M. Hollerbach. Calibrating the human hand for haptic interfaces. Presence: Teleoperators and Virtual Environments, 2 (4), 1993.Google Scholar
  26. Rohling, J. M. Hollerbach, and S. C. Jacobsen. Optimized fingertip mapping: A general algorithm for robotic hand teleoperation. Presence: Teleoperators and Virtual Environments, 2 (3), 1993.Google Scholar
  27. Salcudean, N. M. Wong, and R. L. Hollis. A force-reflecting teleoperation system with magnetically levitated master and wrist. In Proc. of IEEE Int. Conf. on Robotics and Automation, pages 1420–1426, Nice, France, 1992.Google Scholar
  28. Satava. Medicine 2001: the king is dead. In A. M. Digioia, T. Kanade, and R. Taylor, editors, First Int. Symp. on Medical Robotics and Computer Assisted Surgery, pages 2–5. Shadyside Hospital, 1992.Google Scholar
  29. Satava. Robotics, telepresence and virtual reality: a critical analysis of the future. Minimally Invasive Therapy, 1: 357–363, 1992.Google Scholar
  30. Satava and S. R. Ellis. Human interface technology: an essential tool for the modem surgeon. Surgical Endoscopy, Submitted in 1993. In press.Google Scholar
  31. Smith. Remembering in and out of context. J. of Exp. Psychology: Human Learning and Memory, 5 (5): 460–471, 1979.CrossRefGoogle Scholar
  32. Stussi, G. Serra, G. Stoppato Venier, D. De Rossi, M. Gallazzi, and G. Zerbi. Patterning of conducting polymers for sensors through chemical vapour deposition. In The Fifth Int. Meeting on Chemical Sensors, pages 1188–1191, Roma, Italy, July 1994.Google Scholar
  33. Suzuki and O. Hirasa. An approach to artificial muscle using polymer gels formed by micro-phase separation. Advanced in Polymer Science, 110: 242–261, 1993.Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Gaetano Canepa
    • 1
  1. 1.Centro “E. Piaggio”Università di PisaPisaItaly

Personalised recommendations