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

被引：8

作者：

Farkh, Rihem ^{[1
,5
]}

Quasim, Mohammad Tabrez ^{[2
]}

Al Jaloud, Khaled ^{[1
]}

Alhuwaimel, Saad ^{[3
]}

Siddiqui, Shams Tabrez ^{[4
]}

机构：

[1] King Saud Univ, Coll Engn, Muzahimiyah Branch, POB 2454, Riyadh 11451, Saudi Arabia

[2] Univ Bisha, Coll Comp & Informat Technol, Bisha 67714, Saudi Arabia

[3] King Abdulaziz City Sci & Technol, Riyadh, Saudi Arabia

[4] Jazan Univ, Coll Comp Sci & Informat Technol, Dept Comp Sci, Jazan, Saudi Arabia

[5] Tunis El Manar Univ, Natl Engn Sch Tunis, Lab Anal Concept & Control Syst, Dept Elect Engn, LR-11-ES20, Tunis, Tunisia

来源：

CMC-COMPUTERS MATERIALS & CONTINUA | 2021年 / 68卷 / 01期

关键词：

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

D O I：

10.32604/cmc.2021.016600

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1065 / 1079

页数：15

共 22 条

[21]

Siegwart R., 2004, A BRADFORD BOOK, P11

[22]

Takashima Y, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), P251, DOI [10.1109/icma.2019.8816199, 10.1109/ICMA.2019.8816199]

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