Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2018, Article ID 3145436, 8 pages
https://doi.org/10.1155/2018/3145436
Research Article

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

1Department of Electrical Engineering, National Engineering School of Sousse, University of Sousse, BP 264, Erriadh, 4023 Sousse, Tunisia
2Laboratory EμE, Faculty of Sciences of Monastir (FSM), University of Monastir, Av. Ibn El Jazzar Skanes, 5019 Monastir, Tunisia
3Research Unit of Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), University of Monastir, Av. Ibn El Jazzar Skanes, 5019 Monastir, Tunisia

Correspondence should be addressed to Awatef Aouf; moc.liamg@fetawafuoa

Received 30 December 2017; Accepted 6 February 2018; Published 5 March 2018

Academic Editor: José Alfredo Hernández-Pérez

Copyright © 2018 Awatef Aouf et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.