Generalización de las trayectorias de un brazo robótico utilizando primitivas de movimiento dinámico y regresión de procesos gaussianos.

Carlos Andrés Peña Solórzano, José Gabriel Hoyos Gutiérrez, Flavio Augusto Prieto Ortiz

Resumen


Es común encontrar robots realizando tareas en áreas compartidas con humanos, donde se espera que sean capaces de aprender de las acciones realizadas por otros y de adaptarse a nuevas situaciones. En este trabajo, se capturan las trayectorias del brazo de un operario mientras se mueve para agarrar un objeto, realizando seguimiento de articulaciones con el sensor kinect de Microsoft. La técnica utilizada para la codificación de las señales de entrenamiento se denominan primitivas de movimiento dinámico (DMP), mientras que la reconstrucción se realiza mediante regresión de procesos gaussianos (GPR). GPR permite además, generalizar los movimientos de entrenamiento a nuevas trayectorias, cuando cambian tanto la posición inicial de la mano como la ubicación del objeto. La técnica de generalización se compara contra un algoritmo basado en distancia de Mahalanobis y distribución gaussiana, que utiliza los datos de la trayectoria sin codificar, para realizar la estimación. La técnica propuesta presentó bajos tiempos de codificación y errores pequeños con respecto a los valores objetivo al probarlo con 30 puntos de consulta para el valor inicial de la mano, y 30 puntos para la posición final.


Palabras clave


Robótica, aprendizaje por imitación, programación por demostración, primitivas de movimiento dinámico, regresión de procesos gaussianos.

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Referencias


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DOI: https://doi.org/10.24050/reia.v15i29.690

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