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[Roy Glasius]

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          NEURAL NETWORK DYNAMICS FOR PATH
          PLANNING AND OBSTACLE AVOIDANCE.

Roy Glasius,   Andrzej Komoda,   Stan C.A.M. Gielen.
University of Nijmegen.


ABSTRACT
A model of a topologically organized neural network
of a Hopfield type with nonlinear analog neurons is shown
to be very effective for path planning and obstacle avoidance.
This deterministic system can rapidly provide a proper path,
from any arbitrary start position to any target position,
avoiding both static and moving obstacles of arbitrary shape.
The model assumes that an (external) input activates a target
neuron, corresponding to the target position, and specifies
obstacles in the topologically ordered neural map. The path
follows from the neural network dynamics and the neural
activity gradient in the topologically ordered map. The
analytical results are supported by computer simulations to
illustrate the performance of the network.

(Neural Networks preprint)

Roy Glasius, Department Medical Physics and Biophysics, 
University of Nijmegen,     Geert Grooteplein Noord 21,
6525 EZ Nijmegen, The Netherlands,   tel: +31-80615040,
email:roy at mbfys.kun.nl.