4.7 Article

A computational framework of kinematic accuracy reliability analysis for industrial robots

Journal

APPLIED MATHEMATICAL MODELLING
Volume 82, Issue -, Pages 189-216

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2020.01.005

Keywords

Kinematic reliability analysis; Industrial robot; Sparse grid method; Saddlepoint approximation method; Extreme value distribution

Funding

  1. National Key RAMP
  2. D Program of China [2017YFB1301300]
  3. National Natural Science Foundation of China [51905146]
  4. Research Foundation of Education Bureau of Hebei Province of China [QN2019141]
  5. Key RAMP
  6. D Plan Program of Hebei Province [19211808D]
  7. Innovation Funding Project for Graduate Students in Hebei Province [CXZZBS2019034]

Ask authors/readers for more resources

A new computational method to evaluate comprehensively the positional accuracy reliability for single coordinate, single point, multipoint and trajectory accuracy of industrial robots is proposed using the sparse grid numerical integration method and the saddlepoint approximation method. A kinematic error model of end-effector is constructed in three coordinate directions using the sparse grid numerical integration method considering uncertain parameters. The first-four order moments and the covariance matrix for three co-ordinates of the end-effector are calculated by extended Gauss-Hermite integration nodes and corresponding weights. The eigen-decomposition is conducted to transform the interdependent coordinates into independent standard normal variables. An equivalent extreme value distribution of response is applied to assess the reliability of kinematic accuracy. The probability density function and probability of failure for extreme value distribution are then derived through the saddlepoint approximation method. Four examples are given to demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available