Open Access

ARTICLE

Intelligent Autonomous-Robot Control for Medical Applications

by Rihem Farkh^1,2, Haykel Marouani^1,*, Khaled Al Jaloud¹, Saad Alhuwaimel³, Mohammad Tabrez Quasim⁴, Yasser Fouad¹

1 College of Engineering, Muzahimiyah Branch, King Saud University, Riyadh, 11451, Saudi Arabia
2 Laboratory for Analysis, Conception and Control of Systems, Department of Electrical Engineering, National Engineering School of Tunis, Tunis El Manar University, 1002, Tunisia
3 King Abdulaziz City for Science and Technology, Riyadh, 12354, Saudi Arabia
4 College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia

* Corresponding Author: Haykel Marouani. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)

Computers, Materials & Continua 2021, 68(2), 2189-2203. https://doi.org/10.32604/cmc.2021.015906

Received 13 December 2020; Accepted 28 February 2021; Issue published 13 April 2021

Abstract

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control of autonomous medic robots. Health officials aim to curb the spread of COVID-19 among medical, nursing staff and patients by using intelligent robots. We propose an advanced controller for a service robot to be used in hospitals. This type of robot is deployed to deliver food and dispense medications to individual patients. An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction. These criteria were met by using two controllers simultaneously: a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative (PID) controller for automatic steering and speed control.

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Citations