Evolutionary Learning of a Fuzzy Behavior Based Controller for a Nonholonomic Mobile Robot in a Class of Dynamic Environments

This paper presents an approach for evolving optimum behaviors for a nonholonomic mobile robot in a class of dynamic environments. A new evolutionary algorithm reflecting some powerful features in the natural evolutionary process to have flexibility to deal with changes in the environment is used to evolve optimum behaviors. Furthermore, a fuzzy set based multi-objective fitness evaluation function is adopted in the evolutionary algorithm. The multi-objective evaluation function is designed so that it allows incorporating complex linguistic f'eatures that a human observer would desire in the behaviors of the mobile robot movements. To illustrate the effectiveness of the proposed method, simulation results are compared using a conventional evolutionary algorithm.