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Polynomial-Neural-Networks--B ased Mobile Robot Path C. L. Philip Chen and Farid Ahmed

Summary: Polynomial- Neural-Networks--B ased Mobile Robot Path
C. L. Philip Chen and Farid Ahmed
Wright State University
Computer Science & Engineering Department
Dayton, OH 45435.
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) [1] 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.
Path planning in mobile robot navigation is associated with the problem of finding a collision free
and minimal cost path between a start point and a goal point in a given environment. The layout
of the environment may be known a-priori or it may be unknown. A significant amount of work
has been done in the field of path planning for mobile robot in the known and unknown dynamic


Source: Ahmed, Farid - Department of Electrical Engineering and Computer Science, Catholic University of America


Collections: Engineering