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Highly Redundant Sensing in Robotic Systems

  • Julius T. Tou
  • Jens G. Balchen

Part of the NATO ASI Series book series (volume 58)

Table of contents

  1. Front Matter
    Pages I-X
  2. General Theory and Overview

  3. Biological Aspects

  4. Specific Data Fusion Approaches and Examples

    1. Front Matter
      Pages 43-43
    2. J. Raczkowsky, U. Rembold
      Pages 45-54
    3. M. Bergamasco, P. Dario, A. Bicchi, G. Buttazzo
      Pages 55-66
    4. Ren C. Luo, Min-Hsiung Lin
      Pages 67-86
    5. N. Nandhakumar, J. K. Aggarwal
      Pages 87-101
    6. Charles Hansen, Nicholas Ayache, Francis Lustman
      Pages 127-146
  5. Circuits and System Design

    1. Front Matter
      Pages 147-147
    2. V. Cantoni, M. Ferretti, M. Savini
      Pages 157-174
  6. Control Concepts

    1. Front Matter
      Pages 175-175
    2. Gerardo Beni, Jing Wang
      Pages 251-262
    3. Jens G. Balchen, Fredrik Dessen
      Pages 263-275
    4. J. A. Tenreiro Machado, J. L. Martins de Carvalho
      Pages 293-309
    5. A. de Almeida, H. Araujo, J. Dias, L. de Sa, M. Crisostomo, U. Nunes et al.
      Pages 311-320
  7. Back Matter
    Pages 321-325

About these proceedings

Introduction

Design of intelligent robots is one of the most important endeavors in robotics research today. The key to intelligent robot design lies in sensory systems for robotic control and manipulation. In an unstructural environment, robotic sensing translates measurements and characteristics of the environment and working objects into useful information. A robotic system is usually equipped with a variety of sensors to perform redundant sensing and achieve data fusion. This book contains revised versions of papers presented at a NATO Advanced Research Workshop held in Florida in September 1989 within the activities of the NATO Special Programme on Sensory Systems for Robotic Control. The fundamental issues addressed in this volume were: - Theory and techniques, including knowledge-based systems, geometrical fusion, Boolean fusion, probabilistic fusion, feature-based fusion, error-estimation approach, and Markov process modeling. - General concepts, including microscopic redundancy at the sensory element level, macroscopic redundancy at the sensory system level, parallel redundancy, and standby redundancy. - Implementation and application, including robotic control, sensory technology, robotic assembly, robot fingers, sensory signal processing, sensory system integration, and PAPIA architecture. - Biological analogies, including neural nets, pattern recognition, low-level fusion, and motor learning.

Keywords

Motor Sensor cognition control knowledge knowledge-based system knowledge-based systems learning measurement modeling pattern recognition robot robotics sensors signal processing

Editors and affiliations

  • Julius T. Tou
    • 1
  • Jens G. Balchen
    • 2
  1. 1.Center for Information ResearchUniversity of FloridaGainesvilleUSA
  2. 2.Division of Engineering Cybernetics, Norwegian Institute of TechnologyUniversity of TrondheimTrondheimNorway

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-84051-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 1990
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-84053-1
  • Online ISBN 978-3-642-84051-7
  • Buy this book on publisher's site