Intelligent Autonomous-Robot Control for Medical Applications

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 (in...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2021-01, Vol.68 (2), p.2189-2203
Main Authors: Farkh, Rihem, Marouani, Haykel, Al Jaloud, Khaled, Alhuwaimel, Saad, Tabrez Quasim, Mohammad, Fouad, Yasser
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Automatic control
Controllers
Coronaviruses
COVID-19
Health care facilities
Hospitals
Machine learning
Neural networks
Pandemics
Patients
Proportional integral derivative
Robot control
Robots
Service robots
Speed control
Steering
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Summary: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.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.015906