Measurement of Suitability of a Haptic Device in a Virtual Reality System

  • Jose San Martin
  • Gracian Trivino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4563)


In the context of the optimization of the mechanical platform of a virtual reality system involving a haptic device, this paper introduces two tools in order to help the designer for obtaining the best positioning of the device respect to the application workspace. With this purpose we have defined a measure called Average Volumetric Manipulability, of how the application workspace fits in with the volume where haptic device provides its best performance. Also, we have defined other measure called Useful Manipulability which takes in account the frequency with which each zone of the application workspace is visited during the simulation process. The practical use of these measures is demonstrated using them during the design and development of a real application.


Virtual reality Haptic interface Manipulability Mechanical Performance 


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  1. 1.
    Bayona, S., Garcia, M., Mendoza, C., Fernandez, J.M.: Shoulder Arthroscopy Training System with Force Feedback. In: MedVis 2006. International Conference on Medical Information Visualisation-BioMedical Visualisation, pp. 71–76 (2006)Google Scholar
  2. 2.
    Murray, R.M., Li, Z., Sastry, S.S.: A mathematical introduction to robotic manipulation. CRC Press, Inc., Boca Raton, FL (1994)zbMATHGoogle Scholar
  3. 3.
    Yoshikawa, T.: Manipulability and redundancy control of robotic mechanisms, Robotics and Automation. In: Proceedings of IEEE International Conference on March 1985, vol. 2, pp.1004–1009 (1985)Google Scholar
  4. 4.
    Cavusoglu, M.C., Feygin, D., Tendick, F.: A Critical Study of the Mechanical and Electrical Properties of the PHANToM Haptic Interface and Improvements for High Performance Control. Teleoperators and Virtual Environments 11(6), 555–568 (2002)CrossRefGoogle Scholar
  5. 5.
    Yoshikawa, T.: Foundations of Robotics: Analysis and Control. MIT Press, Cambridge, MA (1990)Google Scholar
  6. 6.
    Sobh, T.M., Toundykov, D.Y.: Optimizing the tasks at hand [robotic manipulators]. Robotics & Automation Magazine, IEEE 11(2), 78–85 (2004)CrossRefGoogle Scholar
  7. 7.
    Alqasemi, R.M., McCaffrey, E.J., Edwards, K.D., Dubey, R.V.: Analysis, evaluation and development of wheelchair-mounted robotic arms. Rehabilitation Robotics 2005, ICORR 2005. In: 9th International Conference on June 28-July 1, 2005, pp. 469–472 (2005)Google Scholar
  8. 8.
    Guilamo, L., Kuffner, J., Nishiwaki, K., Kagami, S.: Manipulability optimization for trajectory generation. Robotics and Automation 2006, ICRA 2006. In: Proceedings 2006 IEEE International Conference on May 15-19, 2006, pp. 2017–2022 (2006)Google Scholar
  9. 9.
    San Martin, J., Trivino, G.: Mechanical performance of a manipulator in virtual reality systems. In: 2nd International Conference on Computer Graphics Theory and Applications. GRAPP March 07, 2007 (accepted, in press)Google Scholar
  10. 10.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Aragon, C.R., Johnson, D.S., McGeoch, L.A., Shevon, C.: Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning. Operations Research 39(3), 378–406 (1991)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jose San Martin
    • 1
  • Gracian Trivino
    • 2
  1. 1.Universidad Rey Juan Carlos, 28933 MóstolesSpain
  2. 2.European Centre for Soft Computing, 33600 MieresSpain

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