Análisis Sistemático de Deep Learning y Computer Vision en Robótica Colaborativa: Un Estudio de la Interacción Segura Humano-Robot
DOI:
https://doi.org/10.64784/Palabras clave:
Automatización, Robótica, Inteligencia Artificial, Tecnología de la Información, Seguridad industrialResumen
El presente estudio realiza un análisis sistemático de la literatura científica sobre la integración de Deep Learning, Computer Vision e Internet de las Cosas (IoT) en sistemas de robótica colaborativa, con principal enfoque en la interacción segura entre humanos y robots en entornos industriales. El objetivo principal es identificar los enfoques más avanzados documentados en la literatura reciente, considerando el impacto del IoT en la optimización del procesamiento y la toma de decisiones. La metodología empleada corresponde a una revisión sistemática cualitativa de artículos científicos publicados entre 2020 y 2025, utilizando el Método de Análisis Temático para la organización, clasificación e interpretación de información proveniente de repositorios académicos como IEEE Xplore y Science Direct. Los resultados evidencian que los modelos híbridos CNN-LSTM reportados en la literatura alcanzan precisiones superiores al 85% en detección de gestos y movimientos humanos. La integración de arquitecturas IoT basadas en edge computing reduce significativamente los tiempos de respuesta ante situaciones de riesgo. Sin embargo, se identifican limitaciones asociadas con la variabilidad ambiental, la escasez de datasets especializados y la ausencia de marcos normativos específicos para sistemas basados en inteligencia artificial.
La literatura demuestra que la convergencia de estas tecnologías representa un paradigma innovador para la seguridad industrial, aunque persisten desafíos técnicos y regulatorios que requieren mayor investigación.
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