Robot reliability through fuzzy Markov models

Abstract

In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often inappropriate for this task due to a lack of sufficient probabilistic information during the design and prototyping phases. Fuzzy logic offers an alternative to the probability paradigm, possibility, that is much more appropriate to reliability in the robotic context. Fuzzy Markov modeling, the technique developed in this paper, is a technique for analyzing fault tolerant designs under considerable uncertainty, such as is seen in compilations of component failure rates. It is sufficiently detailed to provide useful information while maintaining the fuzziness (uncertainty) inherent in the situation. It works well in conjunction with fuzzy fault trees, a well-established fuzzy reliability tool. Perhaps most importantly, it builds directly on existing reliability techniques, making it easy to add to reliability toolboxes.