A vision-based approach for intelligent robot navigation

Abstract

In this paper, we present an evolutionary approach for vision-based robot navigation in human environments. In our method, we convert the captured image in a binary one, which after the partition is used as the input of the neural controller. The neural control system, which maps the visual information to motor commands, is evolved online using real robots. We show that evolved neural networks performed well in indoor human environments. Furthermore, we compare the performance of neural controllers with an algorithmic vision-based control method.