No Cover Image

Journal article 277 views

Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots

Chentao Mao, Zhangwei Chen, Shuai Li Orcid Logo, Xiang Zhang

Journal of Intelligent & Robotic Systems, Volume: 101, Issue: 1

Swansea University Author: Shuai Li Orcid Logo

Full text not available from this repository: check for access using links below.

Abstract

Kinematic calibration of robots is an effective way to guarantee and promote their performance characteristics. There are many mature researches on kinematic calibration, and methods based on MDH model are the most common ones. However, when employing these calibration methods, it occasionally happe...

Full description

Published in: Journal of Intelligent & Robotic Systems
ISSN: 0921-0296 1573-0409
Published: Springer Science and Business Media LLC 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa55908
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Kinematic calibration of robots is an effective way to guarantee and promote their performance characteristics. There are many mature researches on kinematic calibration, and methods based on MDH model are the most common ones. However, when employing these calibration methods, it occasionally happens that the objective function cannot converge during iterations. Through analyzing robotic forward kinematics, we found out that the Cartesian coordinates of the end-point are affine to length-related MDH parameters, where linear and nonlinear parameters can be separated. Thanks to the distinctive characteristic of the MDH model, the kinematic calibration problem can be converted into a separable nonlinear least squares problem, which can further be partitioned into two subproblems: a linear least squares problem and a reduced problem involving only nonlinear parameters. Eventually, the optimal structural parameters can be identified by solving this problem iteratively. The results of numerical and experimental validations show that: 1) the robustness during identification procedure is enhanced by eliminating the partial linear structural parameters, the convergence rate is promoted from 68.98% to 100% with different deviation vector pairs; 2) the initial values to be pre-set for kinematic calibration problem are fewer and 3) fewer parameters are to be identified by nonlinear least squares regression, resulting in fewer iterations and faster convergence, where average runtime is reduced from 33.931s to 1.874s.
Keywords: Kinematic calibration; Robustness; Separable nonlinear least squares; Positioning accuracy
College: College of Engineering
Issue: 1