Araştırma Makalesi

Controlling A Robotic Arm Using Handwritten Digit Recognition Software

Cilt: 5 Sayı: 1 29 Mart 2019
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Controlling A Robotic Arm Using Handwritten Digit Recognition Software

Öz

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. 

Anahtar Kelimeler

Kaynakça

  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).

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Onur Öztürk Bu kişi benim
United Kingdom

Yayımlanma Tarihi

29 Mart 2019

Gönderilme Tarihi

21 Eylül 2018

Kabul Tarihi

30 Ocak 2019

Yayımlandığı Sayı

Yıl 2019 Cilt: 5 Sayı: 1

Kaynak Göster

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, ve 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 (01 Mart 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, ve A. Okatan, “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”, IJET, c. 5, sy 1, ss. 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 (01 Mart 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, vd. “Controlling A Robotic Arm Using Handwritten Digit Recognition Software”. International Journal of Engineering Technologies IJET, c. 5, sy 1, Mart 2019, ss. 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. 01 Mart 2019;5(1):15-23. doi:10.19072/ijet.462378

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