A Neural Network Based Navigation for Intelligent Autonomous Mobile Robots

Abstract

in this present work we propose a neural network based navigation for intelligent autonomous mobile robots. Indeed, Neural Networks deal with cognitive tasks such as learning, adaptation generalization and they are well appropriate when knowledge based systems are involved. The adaptation is largely related to the learning capacity since the network is able to take into account and respond to new constraints and data related to the external environments. Just as human being, a neural network relies on previously solved examples to build a system of that makes new decisions, classification and forecasts. 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.