Competitive learning for extraction of visual representations of motion

Examines the application of four competitive learning algorithms to the clustering of simple visual motion for use in the vision system of autonomous mobile robots. The arrangement and properties of the optical sensors used were loosely based on the visual apparatus of a jumping spider. It was found that competitive learning and specifically frequency sensitive competitive learning is able to learn to identify motion in an unsupervised manner. These learned visual representations can then be combined in subsequent processing stages for the development of active robotic vision systems. The unpredictability of a robot´s operating environment and the inherent variations in the properties of physical sensors makes the use of adaptive clustering techniques essential. Both simulated and empirical results involving a modest robot demonstrate that novel motion and stationary position can be expressed as a combination of basic learned motion vectors