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Proceedings Paper

Positioning method using polarization-detecting optical sensor for precision robot systems
Author(s): Masao Sakai; Akira Nagayama; Kunihiko Sasakura
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Paper Abstract

A new positioning method using an optical pick-up sensor is demonstrated for an automated subscriber wire distributing frame at the front-end of a telephone switching station called Automated Main Distributing Frame. The wiring operation has been replaced by automated pin insertion into a matrix board which has recently been developed from printed circuit board technology. This positioning method achieves two-dimensional positioning accuracy of 15 im on dynamic strokes across an area of a few meters square. Optical disk pick-up and printed circuit board technologies are employed to achieve such accuracy on a wide stroke. A sensor employing two polarized lights and three position-sensitive devices can detect three-dimensional displacement and two-dimensional inclination from the target guide pattern on the matrix board. We applied this new positioning method to an automated main distributing frame prototype system. Positioning experimerits performed using a cylindrical coordinate motion mechanism with a stroke of 1 . 8 m have obtained two-dimensional p0- sitioning accuracy within 1 5pm. The results confirm the feasibility of highly accurate positioning and insertion of a small pin into a closely spaced crosspoint hole in a matrix board. This will lead to precise and reliable positioning for an automated main distributing frame in an automated subscriber wiring operation in a telephone switching station.

Paper Details

Date Published: 1 March 1991
PDF: 7 pages
Proc. SPIE 1385, Optics, Illumination, and Image Sensing for Machine Vision V, (1 March 1991); doi: 10.1117/12.25343
Show Author Affiliations
Masao Sakai, NTT Applied Electronics Labs. (Japan)
Akira Nagayama, NTT Applied Electronics Labs. (Japan)
Kunihiko Sasakura, NTT Applied Electronics Labs. (Japan)

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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