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Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback

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Abstract

This paper proposes a neural control integrating stereo vision feedback for driving a mobile robot. The proposed approach consists in synthesizing a suitable inverse optimal control to avoid solving the Hamilton Jacobi Bellman equation associated to nonlinear system optimal control. The mobile robot dynamics is approximated by an identifier using a discrete-time recurrent high order neural network, trained with an extended Kalman filter algorithm. The desired trajectory of the robot is computed during navigation using a stereo camera sensor. Simulation and experimental result are presented to illustrate the effectiveness of the proposed control scheme.

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Authors and Affiliations

  1. CINVESTAV, Unidad Guadalajara, 45015, Guadalajara, Jalisco, Mexico

    Michel Lopez-Franco & Edgar N. Sanchez

  2. CUCEI, Universidad de Guadalajara, 45080, Guadalajara, Jalisco, Mexico

    Alma Y. Alanis & Carlos López-Franco

Authors
  1. Michel Lopez-Franco
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  2. Edgar N. Sanchez
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  3. Alma Y. Alanis
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  4. Carlos López-Franco
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Corresponding author

Correspondence to Edgar N. Sanchez.

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Lopez-Franco, M., Sanchez, E.N., Alanis, A.Y. et al. Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback. Neural Process Lett 43, 425–444 (2016). https://doi.org/10.1007/s11063-015-9427-4

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  • DOI: https://doi.org/10.1007/s11063-015-9427-4

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