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Neural Network Perception for Mobile Robot Guidance Dean A. Pomerleau

Neural Network Perception for Mobile Robot Guidance By Dean A. Pomerleau

Neural Network Perception for Mobile Robot Guidance by Dean A. Pomerleau


Summary

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.

Neural Network Perception for Mobile Robot Guidance Summary

Neural Network Perception for Mobile Robot Guidance by Dean A. Pomerleau

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.

Table of Contents

Foreword. Preface. 1. Introduction. 2. Network Architecture. 3. Training Neworks `On-the-Fly'. 4. Training Networks with Structured Noise. 5. Driving Results and Performance. 6. Analysis of Network Representations. 7. Rule-Based Multi-Network Arbitration. 8. Output Appearance Reliability Estimation. 9. Input Reconstruction Reliability Estimation. 10. Other Applications - the SM2. 11. Other Vision-Based Robot Guidance Methods. 12. Conclusion. Subject Index.

Additional information

NPB9780792393733
9780792393733
0792393732
Neural Network Perception for Mobile Robot Guidance by Dean A. Pomerleau
New
Hardback
Springer
1993-07-31
191
N/A
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