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Author:

Cai, Ligang (Cai, Ligang.) (Scholars:蔡力钢) | Liu, Yingjie (Liu, Yingjie.) | Liu, Zhifeng (Liu, Zhifeng.) (Scholars:刘志峰) | Yang, Congbin (Yang, Congbin.)

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EI Scopus

Abstract:

For the problem that industrial robot have a poor ability to perceive external environment, and not able to change the way of operation and path of transformation timely as working condition changes. This paper selects color sensor as the medium of recognition, introduces the embedded system Raspberry Pi to 6 axis industrial robot grabbing assignments, and puts forward a six axis industrial robot intelligent recognition model with color perception, dynamic decision-making and other multi-function. In the model, the color sensor signal will be resolved by color processing algorithm in embedded system, and consequently color information of material on the conveyor can be obtained. The color information after processing by the embedded system will be converted to digital signals. Then input the digital signal into robot controller. Finally, robot controller chooses corresponding motion path according to the input signal. The experimental results show that the six axis industrial robot intelligent recognition system can distinguish colors of material with almost 100% accuracy, and adjust operation way and path in 0.72 s time rapidly. Efficiency could be improved by more than 75%. © 2017 IEEE.

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Author Community:

  • [ 1 ] [Cai, Ligang]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Liu, Yingjie]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Liu, Zhifeng]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Yang, Congbin]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China

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Source :

Year: 2017

Page: 2836-2841

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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