Research Article
Year 2019,
Volume: 5 Issue: 1, 15 - 23, 29.03.2019
Abstract
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).
- P. Bezak, P. Bozek, Y. Nikitin, Advanced robotic grasping system using deep learning, Procedia Engineering, 96, pp. 10-20, 2014.
- A. Dhawan, A. Bhat, S. Sharma, H. K. Kaura, Automated robot with object recognition and handling features, International Journal of Electronics and Computer Science Engineering, ISSN- 2277-1956.
- E. B. Mathew, D. Khanduja, B. Sapra, B. Bhushan, Robotic arm control through human arm movement detection using potentiometers. 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015.
- B.Iscimen, H. Atasoy, Y. Kutlu, S. Yildirim, E. Yildirim, Smart robot arm motion using computer vision, 2015.
- M.A. Jayaram, H. Fleyeh, Convex Hulls in Image Processing, A Scoping Review, American Journal of Intelligent Systems, 2016.
- N. Rai, B. Rai, P. Rai, Computer vision approach for controlling educational robotic arm based on object properties, 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking. 2014
- T. S. Tonbul, M. Sarıtas, Beş eksenli bir edubot robot kolunda ters kinematic hesaplamalar ve yörünge planlaması. J. Fac. Eng. Arch. Gazi Univ. Vol 18, No 1, 145-167, 2013
- A. B. Rehiara, Kinematics of adeptthree robot arm, Robot Arms, ISBN: 978-953-307-160-2, 2011.
Year 2019,
Volume: 5 Issue: 1, 15 - 23, 29.03.2019
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.
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).
- P. Bezak, P. Bozek, Y. Nikitin, Advanced robotic grasping system using deep learning, Procedia Engineering, 96, pp. 10-20, 2014.
- A. Dhawan, A. Bhat, S. Sharma, H. K. Kaura, Automated robot with object recognition and handling features, International Journal of Electronics and Computer Science Engineering, ISSN- 2277-1956.
- E. B. Mathew, D. Khanduja, B. Sapra, B. Bhushan, Robotic arm control through human arm movement detection using potentiometers. 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015.
- B.Iscimen, H. Atasoy, Y. Kutlu, S. Yildirim, E. Yildirim, Smart robot arm motion using computer vision, 2015.
- M.A. Jayaram, H. Fleyeh, Convex Hulls in Image Processing, A Scoping Review, American Journal of Intelligent Systems, 2016.
- N. Rai, B. Rai, P. Rai, Computer vision approach for controlling educational robotic arm based on object properties, 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking. 2014
- T. S. Tonbul, M. Sarıtas, Beş eksenli bir edubot robot kolunda ters kinematic hesaplamalar ve yörünge planlaması. J. Fac. Eng. Arch. Gazi Univ. Vol 18, No 1, 145-167, 2013
- A. B. Rehiara, Kinematics of adeptthree robot arm, Robot Arms, ISBN: 978-953-307-160-2, 2011.
There are 16 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Publication Date | March 29, 2019 |
| Acceptance Date | January 30, 2019 |
| Published in Issue | Year 2019 Volume: 5 Issue: 1 |
