Evolutionary reactive behavior for mobile robots navigation

Mobile robot's navigation and obstacle avoidance in an unknown environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as evolutionary robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-time recurrent neural networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was analyzed. As a result, controlled mutation do not exhibit major advantages against the noncontrolled one, showing that diversity is more powerful than controlled adaptation.