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

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 pa...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-11
Main Authors: Lv, Zhongyan, Song, Aiguo, Wang, Dongxia, Qiao, GuiFang, He, Shun, Wen, Xiulan, Kang, ChuanShuai
Format: Article
Language:English
Subjects:
Accuracy
Calibration
Computer simulation
Engineering
Global positioning systems
GPS
Kinematics
Manufacturing
Methods
Optimization techniques
Parameter estimation
Parameter uncertainty
Probability distribution
Product specifications
Robotics
Robots
Satellite navigation systems
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/7830489