3D tactile sensor array processed by CNN-UM: A fast method for detecting and identifying slippage and twisting motion

Attila Kis, Ferenc Kovács, Péter Szolgay

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In this paper, we present a fast and efficient technique for detecting and identifying the slippage and twisting motion of touching objects. This kind of action cannot be detected with tactile sensors sensing only the normal (perpendicular) component of the forces acting between surfaces. Our approach utilizes an integrated sensing-processing-actuating system comprising: (1) A 2 × 2 taxel (tactile pixel) array mounted on a two-fingered robot hand, (2) a 64 × 64 CNN-UM (Cellular Neural Network-Universal machine), and (3) a closed-loop controller. This arrangement, along with the proper analogic algorithm, allows detection and the control of the tactile event. It is essential to know and comprehend the forces between contact surfaces and the related 3D pressure fields is essential in many robotic applications discussed in the paper.

Original languageEnglish
Pages (from-to)517-531
Number of pages15
JournalInternational Journal of Circuit Theory and Applications
Volume34
Issue number4
DOIs
Publication statusPublished - Jul 1 2006

Keywords

  • 3D tactile sensor array
  • Analogic algorithms
  • Autonomous robots
  • CNN universal machine
  • Pressure map
  • Sensing-processing-actuating system

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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