Vol.29, No.2, 2021, pp.423-435, doi:10.32604/iasc.2021.016219
OPEN ACCESS
ARTICLE
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:
(This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
Received 22 December 2020; Accepted 24 January 2021; Issue published 16 June 2021
Abstract
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.
Keywords
Neural control system; real-time implementation; navigation environment; and mobile robots
Cite This Article
R. Farkh, K. A. Jaloud, S. Alhuwaimel, M. T. 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.
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.