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dc.contributor.supervisor Kinsner, Witold (Electrical and Computer Engineering) en_US
dc.contributor.author Duan, Tong
dc.date.accessioned 2014-07-10T19:14:18Z
dc.date.available 2014-07-10T19:14:18Z
dc.date.issued 2014-07-10
dc.identifier.uri http://hdl.handle.net/1993/23689
dc.description.abstract An 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.subject Perception-based computing en_US
dc.subject Rough sets en_US
dc.subject Fuzzy sets en_US
dc.subject Multi-scale analysis en_US
dc.subject Indoor navigation en_US
dc.subject Granular computing en_US
dc.subject Small autonomous robots en_US
dc.title Towards a rough-fuzzy perception-based computing for vision-based indoor navigation en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Peters, James (Electrical and Computer Engineering) Leung, Carson Kai-Sang (Computer Science) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2014 en_US


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