Solving robot motion planning problem using Hopfield neural network in a fuzzified environment

Abstract

In this paper, a new approach based on artificial neural networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield neural network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield neural network is not optimal, some algorithms are designed to optimize and generate the final answer.