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.