Hybrid regression model via multivariate adaptive regression spline and online sequential extreme learning machine and its application in vision servo system. (28th June 2022)

• Record Type: Journal Article
• Authors: Zhou, Zhiyu; Ji, Jiangfei; Wang, Yaming; Zhu, Zefei; Chen, Ji
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 3(2022)
• Publication Date: 2022-06-28
• Subjects: Multivariate adaptive regression spline -- online sequential extreme learning machine -- hybrid regression model -- vision servo -- robot manipulator; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298806221108603
• Languages: English
• ISSNs: 1729-8806

Abstract

To solve the problems of slow convergence speed, poor robustness, and complex calculation of image Jacobian matrix in image-based visual servo system, a hybrid regression model based on multiple adaptive regression spline and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error. In MOS-ELM, MARS is used to evaluate the importance of input features and select specific features as the input features of online sequential extreme learning machine, so as to obtain better generalization performance and increase the stability of regression model. Finally, the method is applied to the speed predictive control of the manipulator end effector controlled by image-based visual servo and the prediction of machine learning data sets. Experimental results show that the algorithm has high prediction accuracy on machine learning data sets and good control performance in image-based visual servo.