Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors. (January 2023)

• Record Type: Journal Article
• Authors: Zhang, Dequan; Shen, Shuoshuo; Wu, Jinhui; Wang, Fang; Han, Xu
• Is Part Of: Reliability engineering & system safety. Volume 229(2023)
• Publication Date: 2023-01
• Subjects: Industrial robot -- Trajectory reliability analysis -- Sparse grid technique -- Saddlepoint approximation -- Copula function; AIC Akaike information criterion -- CDF Cumulative distribution function -- D-H Denavit-Hartenberg -- DOF Degree of freedom -- DR Dimension reduction -- EGHI Extended Gauss-Hermite integration -- EVD Extreme value distribution -- FORM First order reliability method -- MCS Monte Carlo Simulation -- MGF Moment generating function -- M-SPA The extreme moments saddlepoint approximation -- PDF Probability density function -- SORM Second order reliability method -- SPA Saddlepoint approximation -- SPGR Sparse grid; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108808
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: An effective reliability analysis method for kinematic trajectory accuracy of industrial robots is proposed. A new copula function-based method is developed to quantify the intercorrelations among discrete points on the kinematic trajectory. The effectiveness and robustness of the proposed method are verified by different trajectories of industrial robots. The proposed method quantifies the intercorrelations within the system, which paves a new avenue for system reliability analysis. Abstract: The kinematic trajectory accuracy reliability is an essential index to evaluate the service performance of industrial robots. This study proposes a new kinematic trajectory accuracy reliability analysis method for industrial robots by integrating the sparse grid technique, the saddlepoint approximation method and copula functions. The novelty of this study lies in comprehending the intercorrelations among single point three-coordinate directional positioning errors and multiple points positioning errors on the kinematic trajectory. To start with, the statistical moments of the positioning errors at an arbitrary point on the trajectory of industrial robots are calculated by the extended sparse grid (SPGR) technique. The single-point positioning accuracy reliability is then evaluated by the saddlepoint approximation (SPA) method. The joint failure probability of positioning accuracy between any two points and the matrix of correlation coefficients of positioning errors among multiple points are subsequently calculated by copula functions. Two methods, namely the boundary theory and the multivariate normal distribution theory, are employed in the current study to calculate the failure probability of kinematic trajectory accuracy. Three examples are implemented to demonstrate the proficiency of the currently proposed methods.