![]() ![]() Mobile robots can move between locations to perform desired and complex tasks. To design and manufacture useful products, intelligent mobile robots combine control, electronic, computer, software, and mechanical engineering. Currently, robots are developing in the direction of high precision, high speed, and stable safety. With the advancement of technology and science and improvement of productivity, robots are increasingly being used in various fields ranging from industry, military, healthcare and related fields, search and rescue, management, and agriculture, allowing humans to accomplish complicated tasks. Keywords: Neural control system real-time implementation navigation environment and mobile robots The experimental results show that it has a good control effect and can extend its application. ![]() The network is trained offline using Keras and implemented on a ATmega32 microcontroller. A multilayered feedforward network with a backpropagation training algorithm is employed. An Arduino embedded platform is used to implement the controller. This study proposes a deep learning controller for line follower mobile robots using complex decision-making strategies. Line follower robots need to adapt accurately, quickly, efficiently, and inexpensively to changing operating conditions. Email: Received: 22 December 2020 Accepted: 24 January 2021Ībstract: A line follower robot is an autonomous intelligent system that can detect and follow a line drawn on floor. Rihem Farkh 1, 4, *, Khaled Al jaloud 1, Saad Alhuwaimel 2, Mohammad Tabrez Quasim 3 and Moufida Ksouri 4ġKing Saud University, Riyadh, 11451, Saudi Arabia 2King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia 3College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia 4Laboratory 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. A Deep Learning Approach for the Mobile-Robot Motion Control System Intelligent Automation & Soft Computing DOI:10.32604/iasc.2021.016219Ī Deep Learning Approach for the Mobile-Robot Motion Control System ![]()
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