6 October 1998 Vision-based approach for learning an elementary navigation behavior
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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.
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Tiziana D'Orazio, Tiziana D'Orazio, Grazia Cicirelli, Grazia Cicirelli, Cosimo Distante, Cosimo Distante, } "Vision-based approach for learning an elementary navigation behavior", Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325778; https://doi.org/10.1117/12.325778
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