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Proceedings Paper

Sensor fusion for the navigation of an autonomous guided vehicle using neural networks
Author(s): Jin Cao; Ernest L. Hall
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Paper Abstract

A sensor fusion method for navigation of an Autonomous Guided Vehicle robot using Artificial Neural Network is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles and landmarks. The low-level sensor fusion technique is used for direct integration of sensor data, resulting in parameter and state estimates. The multi-layered perceptron, with a single hidden layer in neural network structure, and the back- propagation algorithm are employed for the mobile robot's navigation and for obstacle avoidance. The significance of this work lies in the development of a new estimation method for mobile robot obstacle avoidance and guidance.

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); doi: 10.1117/12.325774
Show Author Affiliations
Jin Cao, Univ. of Cincinnati (United States)
Ernest L. Hall, 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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