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Advances in Sciences and Technology
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OPTIMIZATION OF MANIPULATION LOGISTICS USING DATA MATRIX CODES

Authors: Pivarčiová, Elena; Karrach, Ladislav; Tučková, Zuzana;

OPTIMIZATION OF MANIPULATION LOGISTICS USING DATA MATRIX CODES

Abstract

In the paper we deal with optimization of manipulation logistics using Data Matrix codes. Our goal is scanning and decoding Data Matrix codes in real-time. We have designed and verified an efficient computer aided method for location of the Data Matrix codes. This method is also suited to real-time processing and has been verified on a test set of images taken from real industrial world. We have proposed a modified, computationally efficient local thresholding technique that uses local mean and variation under the sliding window. The proposed Data Matrix code localization algorithm utilizes the connecting of the adjoining points into the continuous regions and determining of the boundaries of the outer region and it works in two basic steps: localization of the Finder Pattern and verification of the Timing Pattern. Part of the algorithm deals also with the decoding of the Data Matrix code using external libraries. Data Matrix codes can be used to mark logistic units, parts, warehousing positions, but also for automated robot navigation. Because of their low cost, accuracy, speed, reliability, flexibility and efficiency, as well as the ability to write large amounts of data on a small area, they still have a great advantage in logistics.

[KEGA MS SR 003TU Z-4/2016]

Keywords

logistics, Finder Pattern, Timing Pattern, Data Matrix, localization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold