Robot Reliability Through Fuzzy Markov Models

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dc.citation.bibtexNamemastersthesisen_US
dc.citation.journalTitleMasters Thesisen_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.creatorLeuschen, Martin L.
dc.date.accessioned2007-10-31T00:51:18Z
dc.date.available2007-10-31T00:51:18Z
dc.date.issued1997-04-20
dc.date.modified2003-07-12en_US
dc.date.submitted2001-08-31en_US
dc.descriptionMasters Thesisen_US
dc.description.abstractIn 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 analternative 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.en_US
dc.identifier.citation "Robot Reliability Through Fuzzy Markov Models," <i>Masters Thesis,</i> 1997.
dc.identifier.urihttps://hdl.handle.net/1911/20053
dc.language.isoeng
dc.subjectrobotic reliability*
dc.subjectfault tolerance*
dc.subjectfuzzy logic*
dc.subjectmarkov models*
dc.subject.keywordrobotic reliabilityen_US
dc.subject.keywordfault toleranceen_US
dc.subject.keywordfuzzy logicen_US
dc.subject.keywordmarkov modelsen_US
dc.titleRobot Reliability Through Fuzzy Markov Modelsen_US
dc.typeThesis
dc.type.dcmiText
thesis.degree.levelMasters
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