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
- 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).
Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Authors
Ali Çetinkaya
*
0000-0003-4535-3953
Türkiye
Onur Öztürk
This is me
United Kingdom
Ali Okatan
This is me
0000-0002-8893-9711
Türkiye
Publication Date
March 29, 2019
Submission Date
September 21, 2018
Acceptance Date
January 30, 2019
Published in Issue
Year 2019 Volume: 5 Number: 1
