JRM Vol.24 No.5 pp. 908-916
doi: 10.20965/jrm.2012.p0908


A Basic Study on Biological Signal of Operator During Master-Slave System Control

Yu Okamoto*, Kotaro Tadano**, and Kenji Kawashima**

*Technology & Development Division, YASKAWA Electric Corporation, 12-1 Otemachi, Kokurakita-ku, Kitakyushu 803-8530, Japan

**Precision and Intelligence Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

October 5, 2011
August 28, 2012
October 20, 2012
master-slave, biological signal, position sensorless, predictive arm movement, force estimation

In a master-slave system, operators control the master device based on visual information and sensory feedback. This suggests that the position sensors on the slave side are not always necessary. However, position signals are required to estimate the acting force conveyed to the slave using an observer which realize the force sensorless control. In this paper, we focused on the biological signals of the operator to estimate the slave side’s condition which can be used to estimate the external force. In the early stage of the research, we investigate biological signals just with 1-DOF masterslave system. First, we measured electromyographic (EMG) and gripping force of operators when pushing objects. We verified that predictive signals can be used for estimation of touching the objects. Second, we propose a method, which uses wrist joint torque and Total Contraction Level (TCL) calculated by the EMG when pushing the virtual elastic films, of estimating the acting force conveyed to the slave. We verify that the proposed method can estimate external force under specific conditions with a trained subject.

Cite this article as:
Yu Okamoto, Kotaro Tadano, and Kenji Kawashima, “A Basic Study on Biological Signal of Operator During Master-Slave System Control,” J. Robot. Mechatron., Vol.24, No.5, pp. 908-916, 2012.
Data files:
  1. [1] J. T. Dennerlein, and M. C. Yang, “Haptic Force-Feedback Devices for the Office Computer: Performance and Musculoskeletal Loading Issues,” Human Factors Vol.43, No.2, pp. 278-286, 2001.
  2. [2] P. Pitakwatchara, S. Warisawa, and M. Mitsuishi, “Force feedback augmentation modes in the laparoscopic minimal invasive telesurgical system,” IEEE/ASME Trans. on Mechatronics, Vol.12, No.4, pp. 447-454, 2007.
  3. [3] U. Seibold, B. Kubler, and G. Hirzinger, “Prototype of instrument for minimally invasive surgery with 6-axis force sensing capability,” Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation, pp. 496-501, 2005.
  4. [4] K. Ohnishi, S. Katsura, and T. Shimono, “Motion Control for Real World Haptics,” IEEE Industrial Electronics Magazine, Vol.4, No.2, pp. 16-19, 2010.
  5. [5] K. Tadano and K. Kenji, “Development of a Master Slave System with Force-Sensing Abilities Using Pneumatic Actuators for Laparoscopic,” Advanced Robotics, Vol.24, No.12, pp. 1763-1783, Oct. 2010.
  6. [6] G. D. Hager and S. Hutchinson, “Special Section on Vision-Based Control of Robot Manipulators,” IEEE Trans. Robotics and Automation, Vol.12, No.5, 1996.
  7. [7] B. Nelson and N. Papanikolopoulos, “Special Issue on Visual Servoing,” IEEE Robotics and Automation Mag., Vol.5, No.4, 1998.
  8. [8] E. Malis, “Survey of Vision Based Control,” ENSIETA European Naval Ship Design Short Course, Brest, France, 2002.
  9. [9] F. Lacquaniti and C. Maioli, “The role of preparation in tuning anticipatory and reflex responses during catching,” J. Neurosci., Vol.9, pp. 134-148, 1989.
  10. [10] J. R. Flanagan and J. R. Tresilian, “Grip-load force coupling: A general control strategy for transporting objects,” J. of Experimental Psychology: Human Perception and Performance, Vol.20, No.5, pp. 944-957, 1994.
  11. [11] R. S. Johansson and J. R. Flanagan, “Coding and use of tactile signals from the fingertips in object manipulation tasks,” Nature Reviews Neuroscience, Vol.10, pp. 345-359, 2009.
  12. [12] R. B. Jerard, T. W. Williams, and C. W. Ohlenbusch, “Practical design of an EMG controlled above elbow prosthesis,” in Proc. 1974 Conf. Eng. Devices in Rehabilitation, Boston, MA, pp. 73-77, 1974.
  13. [13] S. Jacobsen, D. F. Knutti, R. T. Johnson, and H. H. Sears, “Development of the Utah artificial arm,” IEEE Trans. Biomed. Eng., Vol.BME-29, pp. 249-269, 1982.
  14. [14] O. Fukuda, T. Tsuji, and M. Kaneko, “An EMG Controlled Robotic Manipulator Using Neural Networks,” Proc. of IEEE Int.Workshop on Robot and Human Communication, pp. 442-447, 1997.
  15. [15] F. Lacquaniti, M. Carrozzo, and N. A. Borghese, “Time-varying mechanical behavior of multi-jointed arm in man,” J. Neurohysiol., Vol.69, No.5, pp. 1443-1464, 1993.
  16. [16] H. Gomi and R. Osu, “Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments,” J. Neuroscience, Vol.18, pp. 8965-8978, 1998.
  17. [17] Kawato2001E. Burdet, R. Osu, D. W. Franklin, T. E. Milner, and M. Kawato, “The central nervous system skillfully stabilizes unstable dynamics by learning optimal impedance,” Nature, Vol.414, pp. 446-449, 2001.
  18. [18] R. Osu and H. Gomi, “Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals,” The American Physiological Society, pp. 1458-1468, 1999.
  19. [19] J. H. Kim, M. Sato, and Y. Koike, “Human Arm Posture Control Using the Impedance Controllability of the Musculo-Skeletal System Against the Alteration of the Environments,” The Institute of Control, Automation and Systems Engineers, Vol.4, No.1, pp. 43-48, 2002.

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