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Zero-offset calibration using a screw theory

Authors: Youngwoo Choi; Joono Cheong; Jin Ho Kyung; Hyunmin Do;

Zero-offset calibration using a screw theory

Abstract

In this paper, we propose an algorithm to easily correct zero-offset in robotic manipulators by identifying screw information. The proposed algorithm improves the accuracy of the robot even if there may be errors in the other geometric parameters. To identify the screw information, the robot is made to create a circular trajectory by rotating a certain joint, while other joints being fixed, and then its endpoints are measured by a laser tracker. The screw information such as rotation axis and a point along the axis is computed through a least-square circle fit. By comparing the resulting screw information from the measurement and that from the kinematic model, we determine the amount of zero-offset in the least square sense. Thus we can enhance the position accuracy by compensating for the determined zero-offset associated with the kinematic model. We show the effectiveness of the proposed algorithm via experimentation using a dual arm robot manipulator.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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