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Proceedings Paper

Optical correlator vision system for a manufacturing robot assembly cell
Author(s): Robert W. Brandstetter; Nils J. Fonneland; R. Zanella; M. Yearwood
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Paper Abstract

An optical correlator system is to be interfaced with an existing robot assembly cell to provide the necessary manufacturing machine vision. In operation various parts are scanned by the vision system which provides identification and location of each pre-selected part. This information is forwarded to the robot controller which translates these data to perform mechanically articulated part retrieval placement and fastening to the assembly being manufactured. A tray of parts would be analyzed with individual parts located for use in an ongoing manufacturing process. As new parts arrive optical memories are fabricated off line and subsequently conveyed to the vision system on demand. To enhance parts identification and location under variable ambient conditions the video from the scanned scene is preprocessed to reduce the effects of lighting and contrast variations. Using laboratory and shop data a variety of actual aircraft parts have been used to fabricate matched filters for use in the vision correlator. Tests conducted with the correlator show that individual parts can be identified located and differentiated from groups of parts with similar appearance. As a next step the correlator as a vision system will be configured for interface with a representative robot cell for premanufacturing tests.

Paper Details

Date Published: 1 March 1991
PDF: 17 pages
Proc. SPIE 1385, Optics, Illumination, and Image Sensing for Machine Vision V, (1 March 1991); doi: 10.1117/12.25368
Show Author Affiliations
Robert W. Brandstetter, Grumman Aerospace Corp. (United States)
Nils J. Fonneland, Grumman Aerospace Corp. (United States)
R. Zanella, Grumman Aerospace Corp. (United States)
M. Yearwood, Grumman Aerospace Corp. (United States)


Published in SPIE Proceedings Vol. 1385:
Optics, Illumination, and Image Sensing for Machine Vision V
Donald J. Svetkoff; Kevin G. Harding; Gordon T. Uber; Norman Wittels, Editor(s)

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