The proposed neural networks navigation approach

Abstract

in this present work we present a neural network navigation approach. To deal with cognitive asks such as learning and generalization; the use of Neural Networks (NN) is necessary to bring the behaviour of Intelligent Autonomous Systems (IAS). Indeed, NNs are well adapted in appropriate form when knowledge based systems are involved. Since the network is able to take into account and respond to new constraints and data related to the external environments, the adaptation here is largely related to the learning capacity. To build a system of "neurons" that makes new decisions, classification and forecasts just as human being, a neural network is relied on previously solved examples. Besides, Networks of neurons can achieve complex classification based on the elementary capability of each neuron to distinguish classes its activation function. In designing a Neural Networks navigation approach, the ability of learning must provide robots with capacities to successfully navigate in the environments like our proposed maze environment. Also, robots must learn during the navigation process, build a map representing the knowledge from sensors, update this one and use it for intelligently planning and controlling the navigation. The simulation results display the ability of the neural networks based approach providing autonomous mobile robots with capability to intelligently navigate in several environments