Assistance control on a haptic system for human adaptive mechatronics

Giving a natural operational feeling to a human via a haptic interface requires not only a sophisticated and intuitive mechanical design, but also an appropriate control system design. Most haptic systems, however, implicitly demand that the human gets used to manipulation of the haptic devices before he/she can get the feel of the virtual space and/or telepresence beyond the haptic device. Based on a new concept of a human-in-the-loop system called Human Adaptive Mechatronics (HAM), an assist-control for a force/vision interactive haptic system is discussed in this paper. The proposed assist-control scheme includes online estimation of a operator's control characteristics, and a 'force assist' function implemented as a change in the support ratio according to the identified skill level. We developed a HAM haptic device test system, performed evaluation experiments with this apparatus and analyzed the measured data. It was confirmed that the operator's skill could be estimated sufficiently and that operator's performance was enhanced by the assist-control.

[1]  Ilana Segall,et al.  Identification of a modified optimal control model for the human operator , 1976, Autom..

[2]  D. Kleinman,et al.  An optimal control model of human response part II: Prediction of human performance in a complex task , 1970 .

[3]  Katsuhisa Furuta,et al.  Control of pendulum: from Super Mechano-System to Human Adaptive Mechatronics , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[4]  Septimiu E. Salcudean,et al.  A virtual excavator for controller development and evaluation , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[5]  Kazuhiro Kosuge,et al.  Virtual internal model following control of robot arms , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[6]  C. L. Sheng,et al.  On identification of synchronous sequential machines , 1972 .

[7]  Fumihito Arai,et al.  Assistance system for crane operation with haptic display - operational assistance to suppress round payload swing , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[8]  Fumihito Arai,et al.  Operational assistance for straight-line operation of rough terrain crane , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[9]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[10]  A. Tustin,et al.  The nature of the operator's response in manual control, and its implications for controller design , 1947 .

[11]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[12]  Vincent Hayward,et al.  Haptic interfaces and devices , 2004 .

[13]  Yoji Yamada,et al.  Proposal of Skill-Assist: a system of assisting human workers by reflecting their skills in positioning tasks , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[14]  Yoshiyuki Tanaka,et al.  Tracking control properties of human-robotic systems based on impedance control , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  Yangsheng Xu,et al.  Human control strategy: abstraction, verification, and replication , 1997 .

[16]  David L. Kleinman,et al.  An optimal control model of human response part I: Theory and validation , 1970 .

[17]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.