期刊
COMPUTERS IN INDUSTRY
卷 58, 期 3, 页码 255-264出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.compind.2006.06.003
关键词
inverse static kinematic calibration; distal supervised learning; Co-evolution
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. (c) 2006 Elsevier B.V. All rights reserved.
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