Imperial College London
Alternate

Professor Yiannis Demiris

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1011Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Su:2014:10.1109/ICRA.2014.6907545,
author = {Su, Y and Dong, W and Wu, Y and Du, Z and Demiris, Y},
doi = {10.1109/ICRA.2014.6907545},
pages = {4692--4698},
title = {Increasing the Accuracy and the Repeatability of Position Control for Micromanipulations Using Heteroscedastic Gaussian Processes},
url = {http://dx.doi.org/10.1109/ICRA.2014.6907545},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Su,Y
AU  - Dong,W
AU  - Wu,Y
AU  - Du,Z
AU  - Demiris,Y
DO  - 10.1109/ICRA.2014.6907545
EP  - 4698
PY  - 2014///
SP  - 4692
TI  - Increasing the Accuracy and the Repeatability of Position Control for Micromanipulations Using Heteroscedastic Gaussian Processes
UR  - http://dx.doi.org/10.1109/ICRA.2014.6907545
UR  - http://hdl.handle.net/10044/1/18614
ER  -
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