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Proceedings Paper

Robot-state detection for visual navigation using a neural network approach
Author(s): Tiziana D'Orazio; Grazia Cicirelli; Arcangelo Distante
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Paper Abstract

In this paper we describe how a robot detects its state with respect to a goal using only visual information and consequently how it learns to reach that goal navigating in a free environment. The state detection is carried out using a feed-forward neural network, with several multiple input and output units, trained using the quickprop method that is an optimized variant of the back-propagation algorithm. The color images captured by the on-board camera of the robot, are a color coded and pre-processed to construct a robust set of inputs to the net, taking account of the trade-off between the dimension of the input set and the loss of information in the image. The simple goal-reaching behavior, finally, is learned using a reinforcement learning algorithm with which the robot associates a proper action to each detected state. To speed up this learning phase an initial state-action mapping is learned in simulation. Starting from this basic knowledge, the real robot will continue to learn the optimal actions for reaching the goal since new unexplored situation can occur in the real environment. The results obtained experimenting this approach on the real robot Nomad200 are described in the paper.

Paper Details

Date Published: 26 August 1999
PDF: 7 pages
Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); doi: 10.1117/12.360293
Show Author Affiliations
Tiziana D'Orazio, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Grazia Cicirelli, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Arcangelo Distante, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)


Published in SPIE Proceedings Vol. 3837:
Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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