Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: Vision based mobile robot guidance has proven difficult for
        classical machine vision methods because of the diversity and
        real time constraints inherent in the task.
        This thesis describes a connectionist system called ALVINN 
        (Autonomous Land Vehicle In a 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
        presents 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.
        
        This thesis present 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.
        
Number: CMU-CS-92-115
Bibtype: TechReport
Month: feb
Author: Dean A. Pomerleau 
Title: Neural Network Perception for Mobile Robot
Year: 1992
Address: Pittsburgh, PA
Super: @CMUTR