Affiliations: Faculty of Engineering, Shahid Chamran University of Ahvaz, Golestan Boulvard, Ahvaz, Iran
Correspondence:
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Corresponding author: Sajjad Fouladvand, Faculty of Engineering, Shahid Chamran University of Ahvaz, Golestan Boulvard, Ahvaz, Iran. Tel.: +98 9163 696 538; E-mail:[email protected]
Abstract: In this paper, an efficient, novel neuro-evolutionary algorithm to navigate
a mobile robot in partially visible environments is introduced. The main
disadvantage of Neuro-Evolutionary algorithm is the slow perception and low
efficiency in complex environments which is required to be developed. This
research is aimed to speed up the iteration and improve the performance in
complicated ambient. In the typical neuro-evolutionary algorithm, random
values are employed either in weights initialization of neural networks or
during the training phase. To do so, this research employed a novel method
in which robot navigation will be done by using selected values by 3 neural
networks rather than one which improve the performance of learning
procedure. Another novel method used in this article is replacing the neural
networks which are responsible for obstacle avoidance by fuzzy algorithm. It
will be shown that fuzzy logic is an easy way to put some initial knowledge
in the neuro-evolution algorithm to avoid learning from zero. The results
clearly demonstrate that the training algorithm approaches the optimum
values with the least iterations which not only reduce the required time for
reaching the target but also materialize the obstacle avoidance aim.
Keywords: Neuro-evolutionary algorithm, mobile robot navigation, adaptive learning algorithms, combination of neural networks, fuzzy systems, infrared sensor, object avoidance
