Open Access

ARTICLE

Computer Vision-Control-Based CNN-PID for Mobile Robot

Rihem Farkh^1,5,*, Mohammad Tabrez Quasim², Khaled Al jaloud¹, Saad Alhuwaimel³, Shams Tabrez Siddiqui⁴
1 College of Engineering, Muzahimiyah Branch, King Saud University, P.O. Box 2454, Riyadh, 11451, Saudi Arabia
2 College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia
3 King Abdulaziz City for Science and Technology, Saudi Arabia
4 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia
5 Department of Electrical Engineering, Laboratory for Analysis, Conception and Control of Systems, LR-11-ES20, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia
* Corresponding Author: Rihem Farkh. Email:
(This article belongs to this Special Issue: Machine Learning for Data Analytics)

Computers, Materials & Continua 2021, 68(1), 1065-1079. https://doi.org/10.32604/cmc.2021.016600

Received 06 January 2021; Accepted 07 February 2021; Issue published 22 March 2021

Abstract

With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed control. Two controllers were used simultaneously to achieve this. Convolutional neural networks (CNNs) are used for target tracking and trajectory prediction, and a proportional-integral-derivative controller is designed for automatic steering and speed control. This study makes use of a Raspberry PI, which is responsible for controlling the robot car and performing inference using CNN, based on its current image input.

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Keywords

Autonomous car; pid control; deep learning; convolutional neural network; differential drive system; raspberry pi

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