Design of pick and place robot with identification and classification object based on RFID using STM32VLDISCOVERY

Pick and Place Robot is a type of robot with "Pick and Place mechanism" designed to ease the displacement of objects from 1 position to another as desired. Some systems in the industries embrace the efficiency which is a production line usually enabled in order to produce several different...

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Bibliographic Details
Published in2017 International Conference on Electrical Engineering and Computer Science (ICECOS) pp. 171 - 176
Main Authors Riyadi, Munawar A., Sudira, Norman, Hanif, M. H., Triwiyatno, Aris
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2017
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Summary:Pick and Place Robot is a type of robot with "Pick and Place mechanism" designed to ease the displacement of objects from 1 position to another as desired. Some systems in the industries embrace the efficiency which is a production line usually enabled in order to produce several different types of objects in turn. Based on these circumstances, Pick and Place robot was designed to have 9 laying position divided into 3 main groups, namely A, B, and C and 2 disposal positions included in group X for objects instead of main group. The sensors used for detecting and classifying objects are using an RFID system (Radio Frequency Identification) because each RFID tag has a unique code and different from another. STM32VLDISCOVERY was used as a main controller for displacement movement of objects in the robot and classifying objects simultaneously. STM32VLDISCOVERY is a development kit integrated with a microcontroller STM32F100RBT6B which has a microprocessor based on ARM Cortex-M3. The classification system uses major ASCII 8th and 9th data from RFID tags to distinguish objects in each group. Based on the test of the entire system, Pick and Place robot successfully worked to move the specimen that has been mounted RFID tag to a predetermined position according to the group of objects and whether the position is empty or not.
DOI:10.1109/ICECOS.2017.8167128