Neural Network Perception for Mobile Robot Guidance: 239 (The Springer International Series in Engineering and Computer Science, 239) - Hardcover

Pomerleau, Dean A.

Hardcover

ISBN 10: 0792393732 ISBN 13: 9780792393733

Publisher: Springer, 1993

Synopsis

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.

About this title

Product Description

Hardcover

Synopsis

Vision-based mobile robot guidance has proved difficult for classical machine vision methods because of the diversity and real-time constraints inherent in the task. This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task.But real world problems like vision-based mobile robot guidance present a different set of challenges for the connectionist paradigm. Among them are: how to develop a general representation from a limited amount of real training data; how to understand the internal representations developed by artificial neural networks; how to estimate the reliability of individual networks; how to combine multiple networks trained for different situations into a single system; how to combine connectionist perception with symbolic reasoning."Neural Network Perception for Mobile Robot Guidance" presents novel solutions to each of these problems.

Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour. The techniques also are shown to generalize to the task of controlling the precise foot placement of a walking robot.

Edition details

• Publisher: Springer
• Publication date: 1993
• Language: English
• ISBN 10: 0792393732
• ISBN 13: 9780792393733
• Binding: Hardcover
• Number of pages: 206