Using self-organizing maps to control physical robots with omnidirectional drives

In many application areas, robots most suitably employ classical PID controllers and the like. In the field of autonomous mobile robots, however, further adaptation features are required in order to adapt to dynamically changing environmental conditions. In recent contests, the particular research area of soccer-playing robots, called RoboCup, has observed the emergence of omnidirectional driven robots. Such drives consist of three independently controllable motors with which a robot can simultaneously perform both translational movements and rotations, which yield a significant advantage in soccer games. This paper describes how a Kohonen-feature-map-based neural network is able to learn the required capabilities and how to adapt to changing environmental conditions