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Proceedings Paper

Vision-based approach for learning an elementary navigation behavior
Author(s): Tiziana D'Orazio; Grazia Cicirelli; Cosimo Distante
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Paper Abstract

Developing elementary behavior is the starting point for the realization of complex systems. We present a learning algorithm that realizes a simple goal-reaching behavior for an autonomous vehicle when no a-priori knowledge of the environment is provided. Information coming from a visual sensor is used to detect a general state of the system. To each state an optimal action is associated using a Q- learning algorithm. As sets of states and actions are limited, a few training trials are sufficient in simulation to learn the optimal policy. During test trials (both in simulated and real environment) fuzzy sets with membership functions are introduced to compute the state of the system and the proper action at the extent of tackling errors in state estimation due to noise in vision measures. Experimental results both in simulated and real environment are shown.

Paper Details

Date Published: 6 October 1998
PDF: 7 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325778
Show Author Affiliations
Tiziana D'Orazio, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Grazia Cicirelli, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Cosimo Distante, Univ. degli Studi di Lecce (Italy)

Published in SPIE Proceedings Vol. 3522:
Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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