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Proceedings Paper

Vision Based Sensing Of Position And Orientation Of Overlapped Variably Shaped Components For Robot Manipulation
Author(s): Aristides Gogoussis; Max Donath
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Paper Abstract

Situations exist in which groups of similar parts are fed into the assembly process as clusters of randomly oriented components. Very limited success has been achieved, to date, in the development of sensors that can determine the position and orientation of a part within a cluster. Consequently, the parts must first be mechanically separated and presented to a robot for manipulation. This is not always feasible due to the nature of the manufacturing process or due to the nature of the part itself. Locating variably shaped components poses a particularly challenging problem for a vision based sensing unit. In the electronic manufacturing environment, this situation arises when the extreme flexibility of the leads of some axial-leaded discrete components results in their random spatial deformation. This effect combined with the possibility of mutual overlapping complicates the recognition and separation task. An efficient strategy for accomplishing such a task has been developed. A mechanical manipulator, a vision system, and a light table are used to detect the polarity of notched capacitors supplied in disordered random patterns with overlaps. The method is based on the recognition of local features that are extracted as a result of the masking of the binary image with a grid of curvilinear polygons, which fragments the image into a mosaic of dispersed information islands. This paper will describe the algorithms which ultimately lead to the derivation of the position and orientation of each individual component. Image processing takes place in parallel to the robot motion. As a consequence of the algorithm speed, the total time of the task implementation is now only bounded by the speed of the mechanical motion.

Paper Details

Date Published: 17 January 1985
PDF: 7 pages
Proc. SPIE 0521, Intelligent Robots and Computer Vision, (17 January 1985); doi: 10.1117/12.946171
Show Author Affiliations
Aristides Gogoussis, University of Minnesota (United States)
Max Donath, University of Minnesota (United States)

Published in SPIE Proceedings Vol. 0521:
Intelligent Robots and Computer Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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