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Registration of a hybrid robot using the Degradation-Kronecker method and a purely nonlinear method

Published online by Cambridge University Press:  28 May 2015

S. J. Yan ,
S. K. Ong  and
A. Y. C. Nee
S. J. Yan
Affiliation:
Mechanical Engineering Department, National University of Singapore, 9 Engineering Drive 1, Singapore117576
S. K. Ong*
Affiliation:
Mechanical Engineering Department, National University of Singapore, 9 Engineering Drive 1, Singapore117576
A. Y. C. Nee
Affiliation:
Mechanical Engineering Department, National University of Singapore, 9 Engineering Drive 1, Singapore117576
*
*Corresponding author. E-mail: mpeongsk@nus.edu.sg
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Summary

Although the registration of a robot is crucial in order to identify its pose with respect to a tracking system, there is no reported solution to address this issue for a hybrid robot. Different from classical registration, the registration of a hybrid robot requires the need to solve an equation with three unknowns where two of these unknowns are coupled together. This property makes it difficult to obtain a closed-form solution. This paper is a first attempt to solve the registration of a hybrid robot. The Degradation-Kronecker (D-K) method is proposed as an optimal closed-form solution for the registration of a hybrid robot in this paper. Since closed-form methods generally suffer from limited accuracy, a purely nonlinear (PN) method is proposed to complement the D-K method. With simulation and experiment results, it has been found that both methods are robust. The PN method is more accurate but slower as compared to the D-K method. The fast computation property of the D-K method makes it appropriate to be applied in real-time circumstances, while the PN method is suitable to be applied where good accuracy is preferred.

Keywords

Hybrid robotRegistrationRobot-world calibrationHand-eye calibrationDegradation-Kronecker (D-K) methodNonlinear error function
Type
Articles
Information
Robotica , Volume 34 , Issue 12 , December 2016 , pp. 2729 - 2740
Copyright
Copyright © Cambridge University Press 2015 

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