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

This work presents a method to classify grasped objects with a multi-fingered robotic hand combining proprioceptive and tactile data in a hybrid descriptor. The proprioceptive data are obtained from the joint positions of the hand and the tactile data are obtained from the contact registered by pressure cells installed on the phalanges. The proposed approach allows us to identify the grasped object by learning the contact geometry and stiness from the readings by sensors. In this work, we show that using bimodal data of different nature along with supervised learning techniques improves the recognition rate. In experimentation, more than 3000 grasps of up to 7 dierent domestic objects have been carried out, obtaining an average F1 score around 95 %, performing just a single grasp. In addition, the generalization of the method has been verified by training our system with certain objects and classifying new, similar ones without any prior knowledge.

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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