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Navegación autónoma reactiva de un robot móvil basada en aprendizaje profundo y comportamientos difusos

10.24050/reia.v21i42.1764
Published 2024-07-10
Articles

Abstract

Objetivo: este estudio tuvo como objetivo desarrollar una arquitectura de control para la navegación autónoma reactiva de un robot móvil mediante la integración de técnicas de Deep Learning y comportamientos difusos basados en el reconocimiento de señales de tráfico. Materiales: la investigación utilizó transfer learning con la red Inception V3 como base para entrenar una red neuronal en la identificación de señales de tráfico. Los experimentos se llevaron a cabo utilizando un Donkey-Car, un robot móvil de código abierto tipo Ackermann, con limitaciones computacionales inherentes. Resultados: la implementación de la técnica de transfer learning arrojó un resultado satisfactorio, logrando una alta precisión del 96.2% en la identificación de señales de tráfico. No obstante, se encontraron desafíos debido a retrasos en los cuadros por segundo (FPS) durante las pruebas, atribuidos a la capacidad computacional limitada de la Raspberry Pi. Conclusiones: al combinar Deep Learning y comportamientos difusos, el estudio demostró la efectividad de la arquitectura de control en mejorar las capacidades de navegación autónoma del robot. La integración de modelos pre-entrenados y lógica difusa proporcionó adaptabilidad y capacidad de respuesta a escenarios de tráfico dinámicos. Investigaciones futuras podrían centrarse en optimizar los parámetros del sistema y explorar aplicaciones en entornos más complejos para avanzar aún más en las tecnologías de robótica autónoma e inteligencia artificial.

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Authors

  • Julián López-Velásquez

    Colombia

  • Gustavo Alonso Acosta-Amaya

    Colombia

  • Jovani Alberto Jimenez-Builes

    Universidad Nacional de Colombia

    https://orcid.org/0000-0001-7598-7696

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