Researching Robot Arm Control System Based On Computer Vision Application And Artificial Intelligence Technology

International Journal of Computer Science and Engineering
© 2021 by SSRG - IJCSE Journal
Volume 8 Issue 1
Year of Publication : 2021
Authors : Hoang Thi Phuong

pdf
How to Cite?

Hoang Thi Phuong, "Researching Robot Arm Control System Based On Computer Vision Application And Artificial Intelligence Technology," SSRG International Journal of Computer Science and Engineering , vol. 8,  no. 1, pp. 24-29, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I1P105

Abstract:

One of the most popular robots in the manufacturing world is the robotic arm. In most cases, robotic arms are programmed and used to perform specific tasks, most common for manufacturing, fabrication, and industrial applications. This article presents a robotic arm control system by recognizing hand gestures from the operator. The system is based on three main steps: locate the hand gesture on the received image, determine the outline of the hand gesture, and recognize this gesture using neural networks and deep learning technology. The use of a region of interest extraction and contour detection reduces computation volume, thereby speeding up hand gesture recognition, making it possible for the robot arm to perform real-time operations. The experimental results show the positive effect of the proposed method.

Keywords:

Artificial intelligence, Deep learning, Robot arm, Computer vision, Edge detection.

References:

[1] A. A. Saraiva, D.B.S Santos, F.C.F Marques Junior, J. V. M. Sousa, N.M. Fonseca Ferreira, and A. Valente,Navigation of quadruped multi robots by gesture recognition using restricted Boltzmann machines, 21st International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Panama City, 09(2018) 309-317.
[2] A. Saraiva, R. Melo, V. Filipe, J. Sousa, N.M Fonseca Ferreira, and A. Valente,Mobile multi-robot manipulation by image recognition, International Journal of Systems Applications, Engineering Development, 12(04)(2018) 63-68.
[3] S. S. Rautaray and A. Agrawal,Vision-based hand gesture recognition for human-computer interaction: a survey, Artificial Intelligence Review, 43(1)(2015) 1–54.
[4] R. Wen, W.-L. Tay, B. P. Nguyen, C.-B. Chng, and C.-K. Chui, “Hand gesture guided robot-assisted surgery based on a direct augmented reality interface, Computer methods and programs in biomedicine, 116(2)(2014) 68–80.
[5] G. Choudhary and C. R. BV,Real-time robotic arm control using
hand gestures,” in High-Performance Computing and Applications (ICHPCA), 2014 International Conference on. IEEE, (2014) 1–3.
[6] F. Parada-Loira, E. Gonz´alez-Agulla, and J. L. Alba-Castro,Hand gestures to control infotainment equipment in cars, Intelligent Vehicles Symposium Proceedings, 2014 IEEE. IEEE, (2014) 1–6.
[7] S. Gupta, P. Molchanov, X. Yang, K. Kim, S. Tyree, and J. Kautz, Towards selecting robust hand gestures for automotive interfaces, in Intelligent Vehicles Symposium (IV), 2016 IEEE. IEEE, (2016) 1350–1357.
[8] Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E .,ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25(2012) 1097-1105.
[9] Nguyen Vinh An,Comparison of Edge Detection Techniques, Vietnam National University Journal of Science: Natural Sciences and Technology, 31(2) (2015) 1-7.
[10] A. D. Kulkarni,Computer vision and fuzzy-neural systems, Prentice Hall PTR, (2001), ch. 2 and ch. 6.
[11] R. Jain, R. Kasturi, and B. G. Schunck.,Machine Vision, McGraw-Hill (1995), ch. 14.
[12] D. A. Forsyth and J. Ponce, “Computer vision: a modern approach,” Prentice Hall Professional Technical Reference, 2002, ch. 15.
[13] G. Bradski, A. Kaehler and V. Pisarevsky, Learning-based computer vision with Intel's open-source computer vision library,Intel Technology Journal, 9,(2005).
[14] C. H. Lampert, H. Nickisch, and S. Harmeling,Learning to detect unseen object classes by between-class attribute transfer, IEEE Computer Vision and Pattern Recognition, (2009) 951-958,.
[15] A. Saxena, J. Driemeyer, and A. Y. Ng,Robotic grasping of novel objects using vision, The International Journal of Robotics Research, 27(2)(2008) 157-173.
[16] R. Szabó and A. Gontean,Full 3D Robotic Arm Control with Stereo Cameras Made in LabVIEW, Federated Conference on Computer Science and Information Systems (FedCSIS), (2013) 37-42.
[17] Y. Hasuda, S. Tshibashi, H. Kozuka, H. Okano and J. Ishikawa,A robot designed to play the game Rock, Paper, Scissors,IEEE Industrial Electronics, (2007) 2065-2070.
[18] Ishikawa Watanabe Lab., University of Tokyo www.k2.t.u tokyo.ac.jp/fusion/Janken/index-e.html.
[19] A. Shaikh, G. Khaladkar, R. Jage, T. Pathak and J. Taili,Robotic arm movements wirelessly synchronized with human arm movements using real-time image processing, IEEE India Educators., Conference (TIIEC), Texas Instruments, (2013) 277-284.
[20] S. Manzoor, R. U. Islam, A. Khalid, A. Samad, and J. Iqbal, “An opensource multi-DOF articulated robotic educational platform for autonomous object manipulation, Robotics and Computer-Integrated Manufacturing, 30(3)(2014) 351-362,.
[21] N. Rai, B. Rai, and P. Rai.,Computer vision approach for controlling educational robotic arm based on object properties, IEEE Emerging Technology Trends in Electronics, Communication, and Networking (ET2ECN), 2nd International Conference, (2014) 1-9.
[22] T. P. Cabré, M. T. Cairol, D. F. Calafell, M. T. Ribes and J. P. Roca, “Project-Based Learning Example: Controlling an Educational Robotic Arm With Computer Vision, Tecnologias del Aprendizaje, IEEE Revista Iberoamericana de, 8(3)(2013) 135-142.
[23] Y. Kutlu, M. Kuntalp and D. Kuntalp,Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes, Expert Systems with Applications,36(4) (2009).
[24] C. M. Bishop,Neural networks for pattern recognition,” Clarendon Press, (1995), ch. 4.
[25] B. Iscimen, H. Atasoy, Y. Kutlu, S. Yildirim, E. Yildirim,Bilgisayar Gormesi Ve Gradyan Inis Algoritmasi Kullanilarak Robot Kol Uygulamasi, Akilli Sistemlerde Yenilikler ve Uygulamalari (ASYU) Sempozyumu, (2014) 136-140.