Application of a multi-DOF ultrasonic servomotor in an auditory tele-existence robot

A multi-degree-of-freedom (DOF) ultrasonic motor can rotate in three DOFs and does not generate noise. In addition, with an appropriate preloading mechanism, it can generate high torque for its size. The multi-DOF ultrasonic motor is, therefore, anticipated for use as a servomotor in the next genera...

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Veröffentlicht in:IEEE transactions on robotics 2005-10, Vol.21 (5), p.790-800
Hauptverfasser: Kawano, H., Ando, H., Hirahara, T., Yun, C., Ueha, S.
Format: Artikel
Sprache:eng
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Zusammenfassung:A multi-degree-of-freedom (DOF) ultrasonic motor can rotate in three DOFs and does not generate noise. In addition, with an appropriate preloading mechanism, it can generate high torque for its size. The multi-DOF ultrasonic motor is, therefore, anticipated for use as a servomotor in the next generation of robots. However, for several reasons, there have been few applications of multi-DOF ultrasonic motors. One reason is the difficulty in designing a proper preloading mechanism for the motor and the limitation of the size of the stators. Another is the difficulty in developing a control algorithm, due to the motor's complex and changing dynamical characteristics, and the serious jaggy motion caused by its very quick response. This paper proposes a preloading mechanism and control algorithm for a multi-DOF ultrasonic motor, considering the motor's application to an actual auditory tele-existence robot, TeleHead. TeleHead is an elaborate dummy head robot that has a 3-DOF neck mechanism. The proposed methods achieve smooth and fast multi-DOF rotating motion of the dummy head with little time delay. In the preloading method, tensioned springs generate high preloading force and make up for the lack of torque by compensating for the resistance torque generated by the inclining motion of the dummy head. In the control algorithm, high-DOF motion is managed by high-frequency switching of the rotating axis, and smooth and quick trajectory tracking motion is achieved by introducing feed-forward control using an inverse model, with multiresolution acquired by feedback-error learning. Experimental results verify the high performance of these methods.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2005.851372