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Neural network perception for mobile robot guidance / Pomerleau, Dean A.

Public ISBD Titre : Neural network perception for mobile robot guidance Type de document : texte imprimé Auteurs : Pomerleau, Dean A., Auteur Editeur : Boston : Kluwer academic publishers Année de publication : 1993 Collection : The kluwer international series in engineering and computer science Sous-collection : Robotics: vision, manipulation and sensors num. 239 Importance : XIV-191p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-0-7923-9373-3 Note générale : Bibliogr. p.[179]-186. Index. Langues : Anglais (eng) Mots-clés : Mobile  robots Neural  networks  (Computer  science) Robots  --  Control  systems Commande  automatique Intelligence  artificielle Robotique Robots  mobiles Réseaux  neuronaux  (informatique) Index. décimale : 62-52 Machine et processus conduits ou contrôlés automatiquement Résumé : 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 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, and 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. Note de contenu : Contents: * Network Architecture. * Training Networks "On-The-Fly". * Training Networks With Structured Noise. * Driving Results and Performance. * Analysis of Network Representations. * Rule-Based Multi-network Arbitration. * Output Appearance Reliability Estimation. * Input Reconstruction Reliability Estimation. * Other Applications. The SM[superscript 2]. * Other Vision-based Robot Guidance Methods. Neural network perception for mobile robot guidance [texte imprimé] / Pomerleau, Dean A., Auteur . - Kluwer academic publishers, 1993 . - XIV-191p. : ill. ; 24 cm. - (The kluwer international series in engineering and computer science. Robotics: vision, manipulation and sensors; 239) . ISBN : 978-0-7923-9373-3 Bibliogr. p.[179]-186. Index. Langues : Anglais (eng) Mots-clés : Mobile  robots Neural  networks  (Computer  science) Robots  --  Control  systems Commande  automatique Intelligence  artificielle Robotique Robots  mobiles Réseaux  neuronaux  (informatique) Index. décimale : 62-52 Machine et processus conduits ou contrôlés automatiquement Résumé : 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 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, and 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. Note de contenu : Contents: * Network Architecture. * Training Networks "On-The-Fly". * Training Networks With Structured Noise. * Driving Results and Performance. * Analysis of Network Representations. * Rule-Based Multi-network Arbitration. * Output Appearance Reliability Estimation. * Input Reconstruction Reliability Estimation. * Other Applications. The SM[superscript 2]. * Other Vision-based Robot Guidance Methods.

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041556	62-52 POM	Papier	Bibliothèque Centrale	Automatique	Disponible