Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
- PDF / 3,094,277 Bytes
- 12 Pages / 595.224 x 790.955 pts Page_size
- 52 Downloads / 188 Views
Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots Chentao Mao1
· Zhangwei Chen1 · Shuai Li2 · Xiang Zhang3
Received: 30 March 2020 / Accepted: 2 October 2020 © Springer Nature B.V. 2020
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 endpoint 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
1 Introduction As essential ingredients of intelligent manufacturing, industrial robots have been widely employed in a broad array of fine-processing scenarios, such as arc welding [1, 2], robotic grasping [3] and machining [4, 5], which requires ultra-accurate positioning of robots. Unfortunately, since the deviations of rod lengths and zero offsets of each joint are introduced during the manufacturing and assembling procedure, the absolute positioning and orientation errors of robotic end-points are unevenly distributed in the Cartesian
Chentao Mao
[email protected] 1
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China
2
College of Engineering, Swansea University, Swansea, UK
3
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
space [6, 7]. Hence, it is urgent to identify the structural parameters of robots through kinematic calibration. There are numerous mature methods and algorithms in the field of kinematic calibration [8–10]. However, in practice, it occasionally happened that the objective function of these kinematic calibration methods cannot render to convergence in some situat
Data Loading...
