Yanyu Su; Wei Dong; Yan Wu; Zhijiang Du; Demiris, Y., "Increasing the accuracy and the repeatability of position control for micromanipulations using Heteroscedastic Gaussian Processes," Robotics and Automation (ICRA), 2014 IEEE International Conference on , vol., no., pp.4692,4698, May 31 2014-June 7 2014 doi: 10.1109/ICRA.2014.6907545
Abstract:
Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degree-of-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.