Robot-state detection for visual navigation using a neural network approach

Tiziana D'Orazio; Grazia Cicirelli; Arcangelo Distante

doi:10.1117/12.360293

26 August 1999 Robot-state detection for visual navigation using a neural network approach

Tiziana D'Orazio, Grazia Cicirelli, Arcangelo Distante

Proceedings Volume 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision; (1999) https://doi.org/10.1117/12.360293
Event: Photonics East '99, 1999, Boston, MA, United States

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.

Citation Download Citation

Tiziana D'Orazio, Grazia Cicirelli, and Arcangelo Distante "Robot-state detection for visual navigation using a neural network approach", Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); https://doi.org/10.1117/12.360293

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