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Neural Network Perception for Mobile Robot Guidance

  • Book
  • © 1993

Overview

Authors:
  1. Dean A. Pomerleau

    1. Carnegie Mellon University, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 239)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Dean A. Pomerleau
    Pages 1-8

  3. Network Architecture

    • Dean A. Pomerleau
    Pages 9-32

  4. Training Networks “On-The-Fly”

    • Dean A. Pomerleau
    Pages 33-50

  5. Training Networks With Structured Noise

    • Dean A. Pomerleau
    Pages 51-69

  6. Driving Results and Performance

    • Dean A. Pomerleau
    Pages 71-83

  7. Analysis of Network Representations

    • Dean A. Pomerleau
    Pages 85-106

  8. Rule-Based Multi-network Arbitration

    • Dean A. Pomerleau
    Pages 107-116

  9. Output Appearance Reliability Estimation

    • Dean A. Pomerleau
    Pages 117-131

  10. Input Reconstruction Reliability Estimation

    • Dean A. Pomerleau
    Pages 133-150

  11. Other Applications - The SM2

    • Dean A. Pomerleau
    Pages 151-159

  12. Other Vision-based Robot Guidance Methods

    • Dean A. Pomerleau
    Pages 161-171

  13. Conclusion

    • Dean A. Pomerleau
    Pages 173-177

  14. Back Matter

    Pages 179-191
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Keywords

About this book

Dean Pomerleau's trainable road tracker, ALVINN, is arguably the world's most famous neural net application. It currently holds the world's record for distance traveled by an autonomous robot without interruption: 21.2 miles along a highway, in traffic, at speedsofup to 55 miles per hour. Pomerleau's work has received worldwide attention, including articles in Business Week (March 2, 1992), Discover (July, 1992), and German and Japanese science magazines. It has been featured in two PBS series, "The Machine That Changed the World" and "By the Year 2000," and appeared in news segments on CNN, the Canadian news and entertainment program "Live It Up", and the Danish science program "Chaos". What makes ALVINN especially appealing is that it does not merely drive - it learns to drive, by watching a human driver for roughly five minutes. The training inputstothe neural networkare a video imageoftheroad ahead and thecurrentposition of the steering wheel. ALVINN has learned to drive on single lane, multi-lane, and unpaved roads. It rapidly adapts to other sensors: it learned to drive at night using laser reflectance imaging, and by using a laser rangefinder it learned to swerve to avoid obstacles and maintain a fixed distance from a row of parked cars. It has even learned to drive backwards.

Authors and Affiliations

  • Carnegie Mellon University, USA

    Dean A. Pomerleau

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