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Calibration using adaptive model complexity for parallel and fiber-driven mechanisms

Published online by Cambridge University Press:  10 July 2015

Tomáš Skopec ,
Zbyněk Šika  and
Michael Valášek
Tomáš Skopec
Affiliation:
CTU in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Technická 4, Praha 6, 166 07, Czech Republic
Zbyněk Šika*
Affiliation:
CTU in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Technická 4, Praha 6, 166 07, Czech Republic
Michael Valášek
Affiliation:
CTU in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Technická 4, Praha 6, 166 07, Czech Republic
*
*Corresponding author. E-mail: [email protected]
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Summary

The paper deals with the development and application of a new adaptive calibration method that extends the geometrical calibration of mechanisms from calibration of only dimensions and kinematical joint positions into the calibration of kinematic joint shape imperfections. Originally unknown nonlinear properties of the kinematical pairs are adaptively included into the kinematical models that are used during the calibration calculation. The method uses description by local linear models and validity functions in order to identify and describe nonlinear properties of the kinematical pairs. During each calibration step, various models with growing complexity are considered before the best model variant is selected to improve calibration results. The method is mainly devoted to the structures with many loops like parallel and fiber-driven parallel mechanisms. The method is applied to parallel mechanism Sliding Star and parallel fiber-driven mechanism Quadrosphere.

Keywords

Adaptive calibration methodNeuro-fuzzy calibration methodNonlinear properties of kinematical pairsAdaptive kinematical modelParallel mechanismsFiber driven mechanisms
Type
Articles
Information
Robotica , Volume 34 , Issue 6 , June 2016 , pp. 1416 - 1435
Copyright
Copyright © Cambridge University Press 2015 

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