<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper presents an approach for controlling a linefollowing robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following robot on a running track with different configurations is then evaluated. The results show that the line-following robot responded more efficiently with an artificial neural network control algorithm than with a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following robot to adapt to environmental conditions and overcome obstacles on the track more effectively.</p>
</abstract>

The paper presents an approach for controlling a linefollowing robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following robot on a running track with different configurations is then evaluated. The results show that the line-following robot responded more efficiently with an artificial neural network control algorithm than with a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following robot to adapt to environmental conditions and overcome obstacles on the track more effectively.

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The paper presents an approach for controlling a linefollowing robot using artificial intelligence algorithms. This study aims to evaluate and validate the...

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Pubblicato online: 13 apr 2024

Pagine: 35 - 42

Ricevuto: 06 set 2023

Accettato: 27 ott 2023

DOI: https://doi.org/10.14313/jamris/1-2024/4

Parole chiave
Robotics, Line-following robot, Artificial neural networks

© 2024 Cesar Minaya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Pubblicato online: 13 apr 2024

Pagine: 35 - 42

Ricevuto: 06 set 2023

Accettato: 27 ott 2023

DOI: https://doi.org/10.14313/jamris/1-2024/4

Parole chiaveRobotics, Line-following robot, Artificial neural networks

© 2024 Cesar Minaya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Robotics, Line-following robot, Artificial neural networks