Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behaviour

This paper presents a novel approach to the run-time detection of faults in autonomous mobile robots, based on simulated predictions of real robot behaviour. We show that although simulation can be used to predict real robot behaviour, drift between simulation and reality occurs over time due to the reality gap. This necessitates periodic reinitialisation of the simulation to reduce false positives. Using a simple obstacle avoidance controller afflicted with partial motor failure, we show that selecting the length of this reinitialisation time period is non-trivial, and that there exists a trade-off between minimising drift and the ability to detect the presence of faults.