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
- Y. Lecun, C. Cortes, C.J.C. Burges, MNIST handwritten digit database, http://yann.lecun.com/exdb/mnist/
- K. Sato, N. Shimoda, Build your own machine-learningpowered robot arm using tensorflow and google cloud Google Cloud blog, 2017.
- Keras documentation, https://keras.io/
- TensorFlow, https://www.tensorflow.org/
- 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.
- OpenCV library document, https://opencv.org/
- K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. Arxiv - Computer Vision and Pattern Recognition .
- S. Raschka, V. Mirajalili, Python machine learning (pp. 341-385).
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Ali Çetinkaya
*
0000-0003-4535-3953
Türkiye
Onur Öztürk
Bu kişi benim
United Kingdom
Ali Okatan
Bu kişi benim
0000-0002-8893-9711
Türkiye
Yayımlanma Tarihi
29 Mart 2019
Gönderilme Tarihi
21 Eylül 2018
Kabul Tarihi
30 Ocak 2019
Yayımlandığı Sayı
Yıl 2019 Cilt: 5 Sayı: 1