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Proceedings Paper

Polynomial-neural-network-based mobile robot path planning
Author(s): C. L. Philip Chen; Farid Ahmed
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Paper Abstract

A polynomial-neural-network-based (PNN-based) path planning with an obstacle avoidance scheme is proposed for mobile robot navigation. The PNN is a feature-based mapping neural network which can be successfully trained to interpolate an unknown function by observing few samples. In this work, a very useful method of data analysis technique called the group method of data handling (GMDH) is used to build the PNN. The built PNNs are used for the path planning of a sonar sensor guided mobile robot. The major advantage of using the PNNs is to efficiently use the environment data and to reduce the computational complexity. Also, in this approach, no preprocessing of range data is required.

Paper Details

Date Published: 11 March 1993
PDF: 7 pages
Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); doi: 10.1117/12.141780
Show Author Affiliations
C. L. Philip Chen, Wright State Univ. (United States)
Farid Ahmed, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 1964:
Applications of Artificial Intelligence 1993: Machine Vision and Robotics
Kim L. Boyer; Louise Stark, Editor(s)

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