Uncertainty Estimation of Robot Geometric Parameters and End-Effecter Position Based on New Generation GPS

Xiulan Wen, Shun He, GuiFang Qiao, Dongxia Wang, Aiguo Song, ChuanShuai Kang and Zhongyan Lv

Mathematical Problems in Engineering, 2019, vol. 2019, 1-11

Abstract:

The robot end-effecter positioning accuracy can be improved by the calibration of robot geometric parameters errors. According to the requirements of new generation geometrical product specification (GPS), the calibration uncertainty should be given when the calibration results are given. In this paper, the modified Denavit-Hartenberg method (MDH) of six-joint series robot is established and the joint movement trajectory method is applied to calibrate the robot geometric parameters. The uncertainty contributors significant are analyzed and the calibration uncertainty of robot geometric parameters is estimated based on the guide to the expression of uncertainty in measurement (GUM). In order to overcome the limitations of GUM for highly nonlinear model and reduce computational cost based on Monte Carlo Simulation (MCS) error estimation, an adaptive MCS (AMCS) is proposed to estimate the uncertainty distribution of robot end-effector position. Simulation and practical example are illustrated and the experiments results confirm that not only can the proposed method evaluate the calibration uncertainty of geometric parameters, but also the uncertainty distribution of end-effecter positions in the whole robot workspace can be estimated by AMCS in which the number of MCS trials can be selected adaptively and the quality of the numerical results can be controlled directly. The proposed method not only can evaluate the uncertainty of six-joint series robot geometric parameters and end-effecter position rapidly and accurately, but also can be popularized to the estimation of calibration uncertainty of other kinds of robot geometric parameters.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7830489

DOI: 10.1155/2019/7830489

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