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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 this 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

R. Farkh, K. Al jaloud, S. Alhuwaimel, M. Tabrez Quasim and M. Ksouri, "A deep learning approach for the mobile-robot motion control system," Intelligent Automation & Soft Computing, 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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