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A neural framework for adaptive robot control

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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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Authors and Affiliations

  1. Institute of Neural Information Processing, University of Ulm, James-Franck-Ring, 89081, Ulm, Germany

    Mohamed Oubbati & Günther Palm

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  1. Mohamed Oubbati
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  2. Günther Palm
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Correspondence to Mohamed Oubbati.

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Oubbati, M., Palm, G. A neural framework for adaptive robot control. Neural Comput & Applic 19, 103–114 (2010). https://doi.org/10.1007/s00521-009-0262-2

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  • DOI: https://doi.org/10.1007/s00521-009-0262-2

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