Behavior learning of multiple mobile robots based on spiking neural networks with a parallel genetic algorithm

Recently, various types of artificial neural networks are applied for behavioral learning of mobile robots in unknown and dynamic environments. In this research, the behavioral learning method based on a spiking neural networks for multiple mobile robots are proposed. The robots learn the forward relationship from sensory inputs to motor outputs. However, the behavioral leaning capability of the robots depends strongly on the network structure and the environments. Therefore, we use a parallel genetic algorithm for updating the network structure through the interaction among robots suitable to the environment. Finally, the effectiveness of the proposed method is discussed through experimental results on behavioral learning for collision avoidance.