An Application of Culture Algorithm for Robot Compliance Parameters Recognition
Industrial robotic manipulators are importance in the industrial. However, they are low accuracy due to the deviation of the mathematical model and the actual manipulator. Moreover, the robot manipulators are not ideal static mechanism. The joints and links of the robots are bended when a pay load i...
Saved in:
Published in | International Journal on Electrical Engineering and Informatics Vol. 17; no. 2; pp. 270 - 277 |
---|---|
Main Author | |
Format | Journal Article |
Language | English |
Published |
Bandung
School of Electrical Engineering and Informatics, Bandung Institute of Techonolgy, Indonesia
01.06.2025
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Industrial robotic manipulators are importance in the industrial. However, they are low accuracy due to the deviation of the mathematical model and the actual manipulator. Moreover, the robot manipulators are not ideal static mechanism. The joints and links of the robots are bended when a pay load is applied. To recognize the kinematic and compliance parameters of the robot manipulator, this study proposed a method to identify these constrains at the same time using the culture algorithm and kinematic calibration. The method could be process in two phases. The kinematic parameters of the robot are identified in the first phase using the conventional kinematic calibration. In the second phase, the culture algorithm is employed for determining the compliance constrains. The two phases are applied repeatedly until convergence. The suggested algorithm is quick converge, it also gives the knowledge of errors, and enhance the accuracy of the robot. The effectiveness of the proposed method is demonstrated by experiment on a YS100 robot, comparing it to conventional kinematic calibration and the process using genetic algorithm to identify stiffness parameters, thereby clarifying the advantage of the proposed method. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 2085-6830 2087-5886 |
DOI: | 10.15676/ijeei.2025.17.2.9 |