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A Deep Learning Approach for the Mobile-Robot Motion Control System

Rihem Farkh1,4,*, Khaled Al jaloud1, Saad Alhuwaimel2, Mohammad Tabrez Quasim3, Moufida Ksouri4

1 King Saud University, Riyadh, 11451, Saudi Arabia
2 King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
3 College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia
4 Laboratory for Analysis, Conception and Control of Systems, LR-11-ES20, Department of Electrical Engineering, National Engineering School of Tunis, Tunis El Manar University, Tunis, 1002, Tunisia

* Corresponding Author: Rihem Farkh. Email: email

(This article belongs to the Special Issue: Machine Learning and Deep Learning for Transportation)

Intelligent Automation & Soft Computing 2021, 29(2), 423-435.


A line follower robot is an autonomous intelligent system that can detect and follow a line drawn on floor. Line follower robots need to adapt accurately, quickly, efficiently, and inexpensively to changing operating conditions. This study proposes a deep learning controller for line follower mobile robots using complex decision-making strategies. An Arduino embedded platform is used to implement the controller. A multilayered feedforward network with a backpropagation training algorithm is employed. The network is trained offline using Keras and implemented on a ATmega32 microcontroller. The experimental results show that it has a good control effect and can extend its application.


Cite This Article

APA Style
Farkh, R., jaloud, K.A., Alhuwaimel, S., Quasim, M.T., Ksouri, M. (2021). A deep learning approach for the mobile-robot motion control system. Intelligent Automation & Soft Computing, 29(2), 423-435.
Vancouver Style
Farkh R, jaloud KA, Alhuwaimel S, Quasim MT, Ksouri M. A deep learning approach for the mobile-robot motion control system. Intell Automat Soft Comput . 2021;29(2):423-435
IEEE Style
R. Farkh, K.A. jaloud, S. Alhuwaimel, M.T. Quasim, and M. Ksouri "A Deep Learning Approach for the Mobile-Robot Motion Control System," Intell. Automat. Soft Comput. , vol. 29, no. 2, pp. 423-435. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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