Design of an Artificial Neural Network Based Tactile Sensor for the UTAH/MIT Dexterous Hand

Abstract

The Neural Tactile Sensor (NTS) is a high resolution, easily manufactured tactile sensor consisting of electrodes, a thin resistive 'skin', and pattern recognition circuitry that is capable of resolving dynamic and static contact location, force, and slip throughout the continuum of the sensor's active region. The sensor operates by means of a resistive 'skin' harboring the electric field generated when a current is injected into it, and a plurality of electrodes for taking measurements of said electric field. When current flows through the resistive medium from the location of tactile contact, an electric field within the resistive medium is established, with a voltage distribution pattern dependent on where the contact was made. The contact generated electric field is measured at a plurality of locations on or in the resistive medium by the electrodes. The outputs of these electrodes can be interpreted as a continuum of field representing voltage patterns that are unique, repeatable, and dependent upon the location that field establishing contact was made. The dynamics of said voltage patterns are related to contact trajectory and slip. These voltage patterns are then used as input to appropriate pattern recognizer circuitry, such as the multilayer perceptron Artificial Neural Network (ANN).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1992
Accession Number
ADA256565

Entities

People

  • Jeffery D. Nering

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Control Systems
  • Detection
  • Electrical Engineering
  • Fabrication
  • Geometric Forms
  • Geometry
  • Grids
  • Information Processing
  • Manufacturing
  • Materials
  • Measurement
  • Mechanics
  • Neural Networks
  • Pattern Recognition
  • Two Dimensional

Readers

  • Neural Network Machine Learning.
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  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks