Research Article

Controlling A Robotic Arm Using Handwritten Digit Recognition Software

Volume: 5 Number: 1 March 29, 2019
EN

Controlling A Robotic Arm Using Handwritten Digit Recognition Software

Abstract

Repetitive tasks in the manufacturing industry is becoming more and more commonplace. The ability to write down a number set and operate the robot using that number set could increase the productivity in the manufacturing industry. For this purpose, our team came up with a robotic application which uses MNIST data set provided by Tensorflow to employ deep learning to identify handwritten digits. The system is equipped with a robotic arm, where an electromagnet is placed on top of the robotic arm. The movement of the robotic arm is triggered via the recognition of handwritten digits using the MNIST data set. The real time image is captured via an external webcam. This robot was designed as a prototype to reduce repetitive tasks conducted by humans. 

Keywords

References

  1. Y. Lecun, C. Cortes, C.J.C. Burges, MNIST handwritten digit database, http://yann.lecun.com/exdb/mnist/
  2. K. Sato, N. Shimoda, Build your own machine-learningpowered robot arm using tensorflow and google cloud Google Cloud blog, 2017.
  3. Keras documentation, https://keras.io/
  4. TensorFlow, https://www.tensorflow.org/
  5. A. Elfasakhany, E. Yanez, K. Baylon, R. Salgado, Design and development of a competitive low-cost robot arm with four degrees of freedom, Modern Mechanical Engineering, pp.47-55.
  6. OpenCV library document, https://opencv.org/
  7. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. Arxiv - Computer Vision and Pattern Recognition .
  8. S. Raschka, V. Mirajalili, Python machine learning (pp. 341-385).

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Onur Öztürk This is me
United Kingdom

Publication Date

March 29, 2019

Submission Date

September 21, 2018

Acceptance Date

January 30, 2019

Published in Issue

Year 2019 Volume: 5 Number: 1

APA
Çetinkaya, A., Öztürk, O., & Okatan, A. (2019). Controlling A Robotic Arm Using Handwritten Digit Recognition Software. International Journal of Engineering Technologies IJET, 5(1), 15-23. https://doi.org/10.19072/ijet.462378
AMA
1.Çetinkaya A, Öztürk O, Okatan A. Controlling A Robotic Arm Using Handwritten Digit Recognition Software. IJET. 2019;5(1):15-23. doi:10.19072/ijet.462378
Chicago
Çetinkaya, Ali, Onur Öztürk, and Ali Okatan. 2019. “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”. International Journal of Engineering Technologies IJET 5 (1): 15-23. https://doi.org/10.19072/ijet.462378.
EndNote
Çetinkaya A, Öztürk O, Okatan A (March 1, 2019) Controlling A Robotic Arm Using Handwritten Digit Recognition Software. International Journal of Engineering Technologies IJET 5 1 15–23.
IEEE
[1]A. Çetinkaya, O. Öztürk, and A. Okatan, “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”, IJET, vol. 5, no. 1, pp. 15–23, Mar. 2019, doi: 10.19072/ijet.462378.
ISNAD
Çetinkaya, Ali - Öztürk, Onur - Okatan, Ali. “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”. International Journal of Engineering Technologies IJET 5/1 (March 1, 2019): 15-23. https://doi.org/10.19072/ijet.462378.
JAMA
1.Çetinkaya A, Öztürk O, Okatan A. Controlling A Robotic Arm Using Handwritten Digit Recognition Software. IJET. 2019;5:15–23.
MLA
Çetinkaya, Ali, et al. “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”. International Journal of Engineering Technologies IJET, vol. 5, no. 1, Mar. 2019, pp. 15-23, doi:10.19072/ijet.462378.
Vancouver
1.Ali Çetinkaya, Onur Öztürk, Ali Okatan. Controlling A Robotic Arm Using Handwritten Digit Recognition Software. IJET. 2019 Mar. 1;5(1):15-23. doi:10.19072/ijet.462378

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