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Proceedings Paper

Comparison of optical modeling and neural networks for robot guidance
Author(s): Sameer Parasnis; Sasanka Velidandla; Ernest L. Hall; Sam Anand
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Paper Abstract

A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-coded algorithms for processing sensor information. Recent techniques involve the use of artificial neural networks to process sensor data for mobile robot guidance. A comparison of a fuzzy logic control for an AGV and a neural network perception is described in this work. A mobile robot test bed has been constructed using a golf cart base. The test bed has a fuzzy logic controller which uses both vision and obstacle information and provides the steering and speed controls to the robot. A feed-forward neural network is described to guide the robot using vision and range data. Suitable criteria for comparison will be formulated and the hand-coded system compared with a connectionist model. A comparison of the two systems, with performance, efficiency and reliability as the criteria, will be achieved. The significance of this work is that it provides comparative tradeoffs on two important robot guidance methods.

Paper Details

Date Published: 6 October 1998
PDF: 9 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998);
Show Author Affiliations
Sameer Parasnis, Univ. of Cincinnati (United States)
Sasanka Velidandla, Univ. of Cincinnati (United States)
Ernest L. Hall, Univ. of Cincinnati (United States)
Sam Anand, Univ. of Cincinnati (United States)


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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