Integration of an Inverse Optimal Neural Controller with Reinforced-SLAM for Path Panning and Mapping in Dynamic Environments

Alma Y. Alanis, Nancy Arana-Daniel, Carlos López-Franco, Edgar Guevara-Reyes

Universidad de Guadalajara, CUCEI, Zapopan, Jalisco, México. almayalanis@gmail.com, nancyaranad@gmail.com, clzfranco@gmail.com, guevara_1@hotmail.com

Corresponding author is Alma Y. Alanis.

Article received on 03/12/2014. ]]> Accepted 24/04/2015.

Abstract

This work presents an artificial intelligence approach to solve the problem of finding a path and creating a map in unknown environments using Reinforcement Learning (RL) and Simultaneous Localization and Mapping (SLAM) for a differential mobile robot along with an optimal control system. We propose the integration of these approaches (two of the most widely used ones) for the implementation of robot navigation systems with an efficient method of control composed by a neural identifier and an inverse optimal control in order to obtain a robust and autonomous system of navigation in unknown and dynamic environments.

Keywords: Optimal neural control, reinforced-SLAM, path panning, mapping, dynamic environments.

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Acknowledgements

The authors are thankful for the support by CONACYT Mexico through Projects 103191Y, 106838Y, 156567Y, and INFR-229696.

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