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Proceedings Paper

A 2D CMAC neural net algorithm for a positioning system of automated agriculture vehicle
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Paper Abstract

In a machine vision-based guidance system, a camera must be corrected precisely to calculate the position of vehicle, however, it is not easy to obtain the intrinsic and extrinsic parameters of the camera, while neural nets have the advantage to set up a mapping relationship for a nonlinear system. We intended to use the CMAC neural net to construct two map relationships: image coordinates and offsets of the vehicle, and image coordinates and the heading angle of the vehicle. The net inputs were the coordinates of top and bottom points in the detected guidance line in the image coordinate system. The outputs were offsets and heading angles. The verified results show that the RMS of inferred offset is 10.5 mm, and the STD is 11.3 mm; the RMS of inferred heading is 1.1°, and the STD is 0.99°.

Paper Details

Date Published: 2 October 2006
PDF: 9 pages
Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 63840Y (2 October 2006); doi: 10.1117/12.686455
Show Author Affiliations
Fangming Zhang, Zhejiang Univ. (China)
Kansas State Univ. (United States)
Yibin Ying, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 6384:
Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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