Evolution of Neural Controllers for Exploration of Dynamic Environments

Environment exploration of dynamic unknown environments is an attractive research issue. In this paper, we propose an evolutionary based method to deal with this issue. We evolved a neural controller that uses the laser data and information of unexplored environment to generate the robot best action. The map of the environment is generated by the robot in a form of a binary matrix. The explored are is used as fitness function of the genetic algorithm. The experimental results show a good performance of the proposed method for exploration of unknown dynamic environments.