A Moment Approach to Positioning Accuracy Reliability Analysis for Industrial Robots

Abstract

The uncertain variables of the link dimensions and joint clearances, whose deviation is caused by manufacturing and assembling errors, have a considerable influence on the positioning accuracy of industrial robots. Understanding how these uncertain variables affect the positioning accuracy of industrial robots is very important to select appropriate parameters during design process. In this paper, the positioning accuracy reliability of industrial robots is analyzed considering the influence of uncertain variables. First, the kinematic models of industrial robots are established based on the Denavit–Hartenberg method, in which the link lengths and joint rotation angles are treated as uncertain variables. Second, the Sobol’ method is used to analyze the sensitivity of uncertain variables for the positioning accuracy of industrial robots, by which the sensitive variables are determined to perform the reliability analysis. Finally, in view of the sensitive variables, the first-four order moments and probability density function of the manipulator's positioning point are assessed by the point estimation method (PEM) in three examples. The Monte Carlo simulation method, the maximum entropy problem with fractional order moments (maximum entropy problem with fractional order moments method (ME-FM) method), and the experimental method are also performed as comparative methods. All the results demonstrate that the proposed PEM has a higher accuracy and efficiency to assess the positioning accuracy reliability of industrial robots.