Clasificación de objetos usando percepción bimodal de palpación única en acciones de agarre robótico

  1. Velasco, E. 1
  2. Zapata-Impata, B.S. 2
  3. Gil, P. 2
  4. Torres, F. 2
  1. 1 Universidad de las Fuerzas Armadas ESPE
  2. 2 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2020

Volumen: 17

Número: 1

Páginas: 44-55

Tipo: Artículo

DOI: 10.4995/RIAI.2019.10923 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

Este trabajo presenta un método para clasificar objetos agarrados con una mano robótica multidedo combinando en un descriptor híbrido datos propioceptivos y táctiles. Los datos propioceptivos se obtienen a partir de las posiciones articulares de la mano y los táctiles se extraen del contacto registrado por células de presión instaladas en las falanges. La aproximación propuesta permite identificar el objeto aprendiendo de forma implícita su geometría y rigidez usando los datos que facilitan los sensores. En este trabajo demostramos que el uso de datos bimodales con técnicas de aprendizaje supervisado mejora la tasa de reconocimiento. En la experimentación, se han llevado a cabo más de 3000 agarres de hasta 7 objetos domésticos distintos, obteniendo clasificaciones correctas del 95%con métrica F1, realizando una única palpación del objeto. Además, la generalización del método se ha verificado entrenando nuestro sistema con unos objetos y posteriormente, clasificando otros nuevos similares.

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Información de financiación

Este trabajo ha sido financiado con Fondos Europeos de Desarrollo Regional (FEDER), Ministerio de Econom?a, Industria y Competitividad a trav?s del proyecto DPI2015-68087-R y la ayuda pre-doctoral BES-2016-078290, y tambi?n gracias al apoyo de la Comisi?n Europea y del programa Interreg V. Sudoe a trav?s del proyecto SOE2/P1/F0638.

Financiadores

    • BES-2016-078290

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Fuente de los datos: Dialnet