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 phase. Fuzzy logic offers an alternative to the probability paradigm, possibility, that is much more appropriate to reliability in the robotic context. This thesis deals with the construction and interpretation of the fault tree and Markov model reliability tools in a possibilistic (fuzzy) context for robotics. Although fuzzy fault trees are well established reliability tools, fuzzy Markov models have not been used in this context. Additionally, the thesis shows how the possibilistic Markov model used in other contexts is inappropriate in the context of fault tolerance, as it does not preserve the uncertainty information contained in the input. A new reliability method involving the joint use of fault trees and Markov models under fuzziness is developed and applied to examples.