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Soft‐computing based navigation approach for a bi‐steerable mobile robot

Ouahiba Azouaoui (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)
Noureddine Ouadah (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)
Ibrahim Mansour (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)
Ali Semani (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)
Salim Aouana (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)
Djafer Chabi (Centre de Développement des Technologies Avancées (CDTA), Baba Hassen, Algeria)

Kybernetes

ISSN: 0368-492X

Article publication date: 1 February 2013

596

Abstract

Purpose

The purpose of this paper is to present an implementation of a soft‐computing (SC) based navigation approach on a bi‐steerable mobile robot, Robucar. This approach must provide Robucar with capability to acquire the obstacle avoidance, target localization, decision‐making and action behaviors after learning and adaptation. This approach uses three neural networks (NN) and fuzzy logic (FL) controller to achieve the desired task. The NNs corresponding to the obstacle avoidance and target localization are trained using the back‐propagation algorithm and the last one is based on the reinforcement learning paradigm while the FL controller uses the Mamdani search and match algorithm. Simulation and experimental results are presented, showing the effectiveness of the overall navigation control system.

Design/methodology/approach

In this paper, an interesting navigation approach is applied to a car‐like robot, Robucar, with addition of an action behavior to deal with the generation of smooth motions. Indeed, this approach is based on four basic behaviors; three of them are fused under a neural paradigm using Gradient Back‐Propagation (GBP) and reinforcement learning (RL) algorithms and the last behavior uses a FL controller. It uses a set of suggested rules to describe the control policy to achieve the action behavior.

Findings

In the implemented SC‐based navigation, the intelligent behaviors necessary to the navigation are acquired by learning using GBP algorithm and adaptation using FL. The proposed approach provides Robucar with more autonomy, intelligence and real‐time processing capabilities. Indeed, the proposed NNs and FLC are able to remedy problems of analytical approaches, missing or incorrect environment knowledge and uncertainties which can lead to undesirable effects as the rough velocity changes. The simulation and experimental results display the ability of the proposed SC‐based navigation approach to provide Robucar with capability to intelligently navigate in a priori unknown environment, illustrating the robustness and adaptation capabilities of the approach.

Research limitations/implications

This work can be extended to consider mobile obstacles with a velocity higher than the velocity of the robot.

Originality/value

This paper presents a learning approach to navigating a bi‐steerable mobile robot in an unknown environment using GBP and RL paradigms.

Keywords

Citation

Azouaoui, O., Ouadah, N., Mansour, I., Semani, A., Aouana, S. and Chabi, D. (2013), "Soft‐computing based navigation approach for a bi‐steerable mobile robot", Kybernetes, Vol. 42 No. 2, pp. 241-267. https://doi.org/10.1108/03684921311310594

Publisher

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Emerald Group Publishing Limited

Copyright © 2013, Emerald Group Publishing Limited

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