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

Journal: Computational intelligence and neuroscience
PMID: 29692803

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.

Authors

  • Awatef Aouf
    Department of Electrical Engineering, National Engineering School of Sousse, University of Sousse, BP 264, Erriadh, 4023 Sousse, Tunisia.
  • Lotfi Boussaid
    Laboratory EμE, Faculty of Sciences of Monastir (FSM), University of Monastir, Av. Ibn El Jazzar Skanes, 5019 Monastir, Tunisia.
  • Anis Sakly
    Research Unit of Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), 5019 Monastir, Tunisia.

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