Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior
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Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) duri... Ver más
1794-1237
2463-0950
21
2024-07-01
4229 pp. 1
14
Revista EIA - 2024
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
