A neural framework for adaptive robot control |
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Authors: | Mohamed Oubbati Günther Palm |
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Affiliation: | (1) Institute of Neural Information Processing, University of Ulm, James-Franck-Ring, 89081 Ulm, Germany |
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Abstract: | This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems
such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks.
The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence
of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach
called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the
optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with
static or moving obstacles. |
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Keywords: | |
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