Bridging the Gap between Robot Simulations and Reality with Improved Models of Sensor Noise

Traditionally sensors have been assumed to behave independently of one another. In this paper evidence is presented that shows that for certain types of sensors this assumption of independence is incorrect. In fact, in some cases groups of sensors respond in a highly correlated fashion. A new model of sensor noise is introduced which combines independent noise with dependent noise to produce sensor responses with varying degrees of correlation. This new model is then compared to the standard model in a set of evolutionary computation experiments. The results reveal that by adopting the new model transfer of simulation results to reality is improved.