Self-organization of reconfigurable processing elements during mobile robots missions

This paper describes a bio-inspired architectural approach to design highly adaptive systems in the context of mobile robotics. The concerned robots evolve in an indoor unknown environment and then exhibit several behaviours such as landscape learning, obstacle avoidance, path planning, sensori-motor control. We aim at designing the intelligent embedded controller of those robots. The controller will be able to self-organize its elements in order to adapt its architecture to the robot behaviour. We focus in this paper on the description of the neural network which was developed to self-organize the system. We present the simulation results of our method.