Uncertainty analysis and evaluation of measurement of the positioning repeatability for industrial robots

摘要

Purpose This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP ( u RP ). Design/methodology/approach Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of u RP . Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the u RP . Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to u RP . Findings Results show that the proposed method can reasonably and objectively estimate the u RP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the u RP of the selected industrial robot can be restricted by using the results of its key factors on u RP . Originality/value This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the u RP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.