Interval methods for fault-tree analysis in robotics

This paper describes a novel technique, based on interval methods, for estimating reliability using fault trees. The approach encodes inherent uncertainty in the input data by modeling these 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 this novel interval method appreciably improves the accuracy of reliability estimates over existing approaches to the problem of uncertain input data. This method avoids the key problem of loss of uncertainty inherent in some approaches when applied to noncoherent systems. It is further shown that the method has advantages over approaches based on partial simulation of the input-data space because it can provide guaranteed bounds for the estimates in reasonable times.