Title: Intelligent mobile robot navigation using a neuro-fuzzy approach

Authors: Somia Brahimi; Ouahiba Azouaoui; Malik Loudini

Addresses: NCRM team, Division Productique and Robotique (DPR), Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algiers, Algeria; LCSI Laboratory, École nationale Supérieure d'Informatique (ESI ex. INI), Oued Smar, Algiers, Algeria ' NCRM team, Division Productique and Robotique (DPR), Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algiers, Algeria ' LCSI Laboratory, École nationale Supérieure d'Informatique (ESI ex. INI), Oued Smar, Algiers, Algeria

Abstract: This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Keywords: mobile robots; autonomous systems; intelligent navigation; fuzzy logic; neural networks; obstacle avoidance; targets seeking; fuzzy Artmap; Mamdani model.

DOI: 10.1504/IJCAET.2019.102500

International Journal of Computer Aided Engineering and Technology, 2019 Vol.11 No.6, pp.710 - 726

Received: 09 Jan 2017
Accepted: 20 Jun 2017

Published online: 04 Jul 2019 *

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