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Solving the motion planning problem by using neural networks

Published online by Cambridge University Press:  09 March 2009

R.H.T. Chan ,
P.K.S. Tam  and
D.N.K. Leung
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Summary

This paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

Keywords

Motion planningNeural networkConfiguration spaceDistance transform.
Type
Article
Information
Robotica , Volume 12 , Issue 4 , July 1994 , pp. 323 - 333
Copyright
Copyright © Cambridge University Press 1994

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