Name: neural network perception for mobile robot guidance
SKU: 3620043
Price: 3375.00 TWD
Availability: InStock
Author: pomerleau

portada neural network perception for mobile robot guidance

neural network perception for mobile robot guidance

Type

Physical Book

Publisher

Springer

Author

pomerleau

Language

English

Pages

208

ISBN

0792393732

ISBN13

9780792393733

Synopsis "neural network perception for mobile robot guidance"

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.