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Intelligent Industrial Systems
Erschienen in: Intelligent Industrial Systems 1/2017

13.03.2017 | Original Paper

Visual Processing and Classification of items on Moving Conveyor with Pick and Place Robot using PLC

verfasst von: Sorayya Kazemi, Hamed Kharrati

Erschienen in: Intelligent Industrial Systems | Ausgabe 1/2017

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Abstract

Many industrial applications require some sort of automated visual processing and classification of items placed on moving conveyor. This paper describes the design and implementation of a robot control system on a hardware platform based on a programmable logic controllers (PLC). The controlled vision based robot is designed for high-performance pick and place applications in packaging work cell. The system includes a pick and place robot whereby a vision system is integrated in its workspace to identify work pieces with respect to their shape and color. A personal computer, operating under the windows platform, carries out all vision related processing and motion planning for the robot. Relevant motion information is communicated through the serial port of the computer to programmable logic controller (PLC), which interfaces with the sensing and actuation devices for robot control. The controlling center of this robot is PLC 7035. After sensing and recognizing the objects features, MATLAB sends results to PLC. PLC sends related orders to robot’s joints, which are made of DC and stepper motors. The robot picks the object, locates it in its respective destination, and turns back to its reference location. The validity of the developed robot is verified through experimental results.
Vorheriger Artikel Flying Vision System Design of Placement Machine for Microphone
Nächster Artikel A Fast and Simple Drowsiness Detection System Based on ARM Microcontrollers

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Metadaten
Titel
Visual Processing and Classification of items on Moving Conveyor with Pick and Place Robot using PLC
verfasst von
Sorayya Kazemi
Hamed Kharrati
Publikationsdatum
13.03.2017
Verlag
Springer Singapore
Erschienen in
Intelligent Industrial Systems / Ausgabe 1/2017
Print ISSN: 2363-6912
Elektronische ISSN: 2199-854X
DOI
https://doi.org/10.1007/s40903-017-0071-3

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