Uncertainty evaluation of measurement of orientation repeatability for industrial robots
Purpose This paper aims to propose a reasonable method to evaluate uncertainty of measurement of industrial robots' orientation repeatability and solve the non-linear problem existing in its evaluation procedure. Design/methodology/approach Firstly, a measurement model of orientation repeatabil...
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Veröffentlicht in: | Industrial robot 2020-01, Vol.47 (2), p.207-217 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Purpose This paper aims to propose a reasonable method to evaluate uncertainty of measurement of industrial robots' orientation repeatability and solve the non-linear problem existing in its evaluation procedure. Design/methodology/approach Firstly, a measurement model of orientation repeatability, based on laser tracker, is established. Secondly, some factors, influencing the measurement result of orientation repeatability, are identified, and their probability distribution functions are modelled. Thirdly, based on Monte Carlo method, an uncertainty evaluation model and algorithm of measurement of industrial robot's orientation repeatability are built. Finally, an industrial robot is taken as the research object to validate the rationality of proposed method. Findings Results show that the measurement model of orientation repeatability of industrial robot is non-linear, and the proposed method can reasonably and objectively estimate uncertainty of measurement of industrial robots' orientation repeatability. Originality/value This paper, based on Monte Carlo method and experimental work, proposes an uncertainty evaluation method of measurement of industrial robots' orientation repeatability which can solve the non-linear problem and provide a reasonable and objective evaluation. And the stochastic ellipsoid approach is firstly taken to model the repeatability of laser tracker. Additionally, this research is beneficial to decide whether the orientation repeatability of the industrial robot meets its requirements. |
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ISSN: | 0143-991X 1758-5791 |
DOI: | 10.1108/IR-07-2019-0145 |