@Article{iasc.2021.016219,
AUTHOR = {Rihem Farkh, Khaled Al jaloud, Saad Alhuwaimel, Mohammad Tabrez Quasim, Moufida Ksouri},
TITLE = {A Deep Learning Approach for the Mobile-Robot Motion Control System},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {29},
YEAR = {2021},
NUMBER = {2},
PAGES = {423--435},
URL = {http://www.techscience.com/iasc/v29n2/42936},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2021.016219}
}