Robot calibration is a widely studied area for which a
variety of solutions have been generated. Most of the
methods proposed address the calibration problem by
establishing a model structure followed by indirect,
often ill-conditioned numeric parameter identification.
This paper introduces a new inverse static kinematic
calibration technique based on genetic programming,
which is used to establish and identify model structure
and parameters. The technique has the potential to
identify the true calibration model avoiding the
problems of conventional methods. The fundamentals of
this approach are described and experimental results
provided.
%0 Journal Article
%1 Dolinsky:2007:CI
%A Dolinsky, J. U.
%A Jenkinson, I. D.
%A Colquhoun, G. J.
%D 2007
%I Elsevier Science Publishers B. V.
%J Computers in Industry
%K Co-evolution Distal Inverse algorithms, calibration, genetic kinematic learning, programming, static supervised
%N 3
%P 255--264
%R doi:10.1016/j.compind.2006.06.003
%T Application of genetic programming to the calibration
of industrial robots
%V 58
%X Robot calibration is a widely studied area for which a
variety of solutions have been generated. Most of the
methods proposed address the calibration problem by
establishing a model structure followed by indirect,
often ill-conditioned numeric parameter identification.
This paper introduces a new inverse static kinematic
calibration technique based on genetic programming,
which is used to establish and identify model structure
and parameters. The technique has the potential to
identify the true calibration model avoiding the
problems of conventional methods. The fundamentals of
this approach are described and experimental results
provided.
@article{Dolinsky:2007:CI,
abstract = {Robot calibration is a widely studied area for which a
variety of solutions have been generated. Most of the
methods proposed address the calibration problem by
establishing a model structure followed by indirect,
often ill-conditioned numeric parameter identification.
This paper introduces a new inverse static kinematic
calibration technique based on genetic programming,
which is used to establish and identify model structure
and parameters. The technique has the potential to
identify the true calibration model avoiding the
problems of conventional methods. The fundamentals of
this approach are described and experimental results
provided.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Dolinsky, J. U. and Jenkinson, I. D. and Colquhoun, G. J.},
biburl = {https://www.bibsonomy.org/bibtex/24c51ad9aed62951ccb94c3a3fb1a32e2/brazovayeye},
doi = {doi:10.1016/j.compind.2006.06.003},
interhash = {b2c5fab522942209a6580b40711da6d5},
intrahash = {4c51ad9aed62951ccb94c3a3fb1a32e2},
issn = {0166-3615},
journal = {Computers in Industry},
keywords = {Co-evolution Distal Inverse algorithms, calibration, genetic kinematic learning, programming, static supervised},
month = {April},
notes = {Codeplay Ltd., Edinburgh, UK
School of Engineering, Liverpool John Moores
University, Byrom Street, Liverpool L3 3AF, UK},
number = 3,
pages = {255--264},
publisher = {Elsevier Science Publishers B. V.},
publisher_address = {Amsterdam, The Netherlands},
timestamp = {2008-06-19T17:38:50.000+0200},
title = {Application of genetic programming to the calibration
of industrial robots},
volume = 58,
year = 2007
}