Towards a rough-fuzzy perception-based computing for vision-based indoor navigation

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dc.contributor.authorDuan, Tong
dc.contributor.examiningcommitteePeters, James (Electrical and Computer Engineering) Leung, Carson Kai-Sang (Computer Science)en_US
dc.contributor.supervisorKinsner, Witold (Electrical and Computer Engineering)en_US
dc.date.accessioned2014-07-10T19:14:18Z
dc.date.available2014-07-10T19:14:18Z
dc.date.issued2014-07-10
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractAn indoor environment could be defined by a complex layout in a compact space. Since mobile robots can be used as substitute for human beings to access harmful and inaccessible locations, the research of autonomous indoor navigation has attracted much interest. In general, a mobile robot navigates in an indoor environment where acquired data are limited. Furthermore, sensor measurements may contain errors in a number of situations. Therefore, the complexity of indoor environment and ability of sensors have determined that it is an insufficient to merely compute with data. This thesis presents a new rough-fuzzy approach to perception-based computing for an indoor navigation algorithm. This approach to perceptual computing is being developed to store, analyze and summarize existing experience in given environment so that the machine is able to detect current situation and respond optimally. To improve uncertainty reasoning of fuzzy logic control, a rough set theory is integrated to regulate inputs before applying fuzzy inference rules. The behaviour extraction is evaluated and adjusted through entropy-based measures and multi-scale analysis. The rough-fuzzy based control algorithm aims to minimize overshoot and optimize transient-state period during navigation. The proposed algorithm is tested through simulations and experiments using practical common situations. The performance is evaluated with respect to desired path keeping and transient-state adaptability.en_US
dc.description.noteOctober 2014en_US
dc.identifier.urihttp://hdl.handle.net/1993/23689
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectPerception-based computingen_US
dc.subjectRough setsen_US
dc.subjectFuzzy setsen_US
dc.subjectMulti-scale analysisen_US
dc.subjectIndoor navigationen_US
dc.subjectGranular computingen_US
dc.subjectSmall autonomous robotsen_US
dc.titleTowards a rough-fuzzy perception-based computing for vision-based indoor navigationen_US
dc.typemaster thesisen_US
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