Interval methods for fault-tree analysis in robotics

Conclusions This paper describes a novel technique, based on interval methods, for the estimation of reliability using fault trees. The approach encodes inherent uncertainty in the input data by modeling this data in terms of intervals. Appropriate interval arithmetic is then used to propagate the data through standard fault trees to generate output distributions which reflect the uncertainty in the input data. Through a canonical example of reliability estimation for a robot manipulator system, we show how the use of our novel interval method significantly improves the accuracy of reliability estimates over existing approaches to the problem of uncertain input data. We also show that the method avoids the key problem of loss of uncertainty inherent in some previously suggested approaches when applied to non-coherent systems. We further discuss how the method has advantages over approaches based on partial simulation of the input data space as it can provide guaranteed bounds for the estimates in reasonable times.