The Behavioural and Topological Effects of Measurement Noise on Evolutionary Neurocontrollers

Abstract

The disparity in performance between simulated and real systems is a major problem in robotics and other fields. Simulating measurement noise is one method of reducing these performance differences. Here, we examine the effect of measurement noise on the behaviour and topology of evolved neuromodulated neurocontrollers applied to control evader agents in a pursuit-evasion game. Measurement noise in the form of a zero-mean, normally distributed random signal is applied to the evader’s radar range and angle signals. The results indicate that increasing the levels of measurement noise increases the number of generations required to evolve fit agents. Noise at the neurocontroller outputs is of lesser amplitude than that at the inputs, suggesting a low-pass filtering operation. When levels of measurement noise different to those with which they were evolved were applied to the neurocontrollers, greater amplitude in the measurement noise signal increased the average length of time required to capture the evader. When the level of measurement noise was changed during evolution, after a few generations of further evolution, the neurocontrollers were able to adapt to both increases and decreases in the amount of noise. The evolutionary neurocontrollers are robust to high levels of measurement noise and can adapt to large changes in noise amplitude. This suggests that the neurocontrollers will be robust when used in the field on real robots, and that they may be a good solution to bridging the gap between simulation and reality.