TY - GEN
T1 - Traffic Signal Classifier for Mobile Robot Navigation Control
AU - Marroquin, Alberto
AU - Farias, Gonzalo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The current computational advance allows the development of technological solutions to everyday problems, for example, today mobile robots are used for the automated transport of raw materials. Therefore, the study of applications such as obstacle avoidance and autonomous navigation with mobile robots are of great importance in academia, driving research that seeks to contribute to scalable and low-cost solutions with mobile robots. Next, a proposal to use artificial vision and add classification functionalities to the navigation control of a mobile robot is described. The solution consists of integrating an embedded system into the mobile robot's navigation control which, through a classification model, identifies the closest traffic signal and then sends a command to the mobile robot so that it modifies its navigation. This work seeks to verify the technical feasibility of using electronic development boards to develop Machine Learning solutions, by obtaining a classifier with an accuracy of 94.9% and a loss rate of 0.21.
AB - The current computational advance allows the development of technological solutions to everyday problems, for example, today mobile robots are used for the automated transport of raw materials. Therefore, the study of applications such as obstacle avoidance and autonomous navigation with mobile robots are of great importance in academia, driving research that seeks to contribute to scalable and low-cost solutions with mobile robots. Next, a proposal to use artificial vision and add classification functionalities to the navigation control of a mobile robot is described. The solution consists of integrating an embedded system into the mobile robot's navigation control which, through a classification model, identifies the closest traffic signal and then sends a command to the mobile robot so that it modifies its navigation. This work seeks to verify the technical feasibility of using electronic development boards to develop Machine Learning solutions, by obtaining a classifier with an accuracy of 94.9% and a loss rate of 0.21.
KW - Classifier
KW - Embedded System
KW - Machine Learning
KW - OpenMV
KW - Traffic Signs
UR - http://www.scopus.com/inward/record.url?scp=85147094641&partnerID=8YFLogxK
U2 - 10.1109/ICA-ACCA56767.2022.10005933
DO - 10.1109/ICA-ACCA56767.2022.10005933
M3 - Conference contribution
AN - SCOPUS:85147094641
T3 - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022
BT - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022
Y2 - 24 October 2022 through 28 October 2022
ER -
