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Object Recognition and Selective Handling by a Robot

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Abstract

A small flexible production cell has been built around a selectively compliant articulated robot arm. Moving on a conveyor belt, boxes marked with different labels are presented to the robot in a random order. Using a camera and a vision card, the labels on the boxes are recognized. Each one of the labels can be rotated, translated or scaled. Three different invariant feature extraction methods (signature, invariant moments of Hu and Zernike) are compared. A neural net is used to classify the labels. The task of the SCARA robot is to pick up the moving boxes and to sort them according to their labels.

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References

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Authors and Affiliations

  1. Department of Control Engineering and Automation, University Gent, Technologiepark 9, 9052, Zwijnaarde, Belgium

    F. Declercq & R. De Keyser

Authors
  1. F. Declercq
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  2. R. De Keyser
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Declercq, F., De Keyser, R. Object Recognition and Selective Handling by a Robot. Journal of Intelligent and Robotic Systems 25, 121–132 (1999). https://doi.org/10.1023/A:1008088209663

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  • DOI: https://doi.org/10.1023/A:1008088209663

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