Open Access
ARTICLE
A Deep Learning Approach for the Mobile-Robot Motion Control System
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)
Intelligent Automation & Soft Computing 2021, 29(2), 423-435. https://doi.org/10.32604/iasc.2021.016219
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
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.