Realtime-learning on an autonomous mobile robot with neural networks

Abstract

We discuss the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world-model has to be adapted by the flow of sensor-samples without the possibility to stop this data flow. Our experiments are done in a simulation environment as well as on a robot, called ALICE.<>