Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13308
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dc.coverage.spatialComputer Scienceen_US
dc.date.accessioned2013-11-27T07:15:12Z-
dc.date.available2013-11-27T07:15:12Z-
dc.date.issued2013-11-27-
dc.identifier.urihttp://hdl.handle.net/10603/13308-
dc.description.abstractSuccessful autonomous robot exploration and navigation requires control algorithms that are capable of navigation through complex unknown environments. These control algorithms must be able to successfully avoid newlinedensely spaced obstruction while navigating a relatively efficient but safe path. Navigation through an indoor and outdoor autonomously attracts special interest newlinefrom various fields. Behavior-based control systems address this task without the need for prior information, these systems deviate from the general mapping approach by eliminating modelling and attempting to make the most direct matching between perception and action. The major components of behavior based systems are a set of independent parallel behaviors and an arbitration or fusion method for combining the behavioral newlinereactions to from a single desired control output. The arbitration problem is the newlinemost challenging problem to the behavior-based navigation, mainly due to the increased demand of complex environments. The major arbitration and fusion approaches are the sub-assumptive and the motor schema approaches. Subsumptive systems switch between behaviors based on the context while motor schema-based architectures combine behavior reactions with vector addition. newlineThe early approaches of behavior based systems used arbitration behaviors in newlinewhich behaviors respond to its stimulus by evoking a single control command. Poor arbitration in these systems led to the development of fusion based behavior newlinesystems in which each behavior respond to its stimulus by combining several control commands. Two approaches to fusion based behavior systems have been proposed the crisp logic approach and the fuzzy logic approach. The fuzzy logic approach is not only simple but also adds robustness to unreliable sensor newlineinformation and encourages smooth control.en_US
dc.format.extent130p.en_US
dc.languageEnglishen_US
dc.relation96en_US
dc.rightsuniversityen_US
dc.titleThe development of fuzzy logic based navigation algorithm for multi-featured autonomous roboten_US
dc.creator.researcherHarisha S Ken_US
dc.subject.keywordComputer Scienceen_US
dc.subject.keywordFuzzy logicen_US
dc.subject.keywordAutonomous roboten_US
dc.description.noteBibliography p. 90-97, Appendix includeden_US
dc.contributor.guideRamakanth Kumar Pen_US
dc.publisher.placeCoimbatoreen_US
dc.publisher.universityAvinashilingam Deemed University For Womenen_US
dc.publisher.institutionDepartment of Computer Scienceen_US
dc.date.registeredOctober, 2007en_US
dc.date.completedNovember, 2012en_US
dc.date.awardedMay, 2013en_US
dc.format.dimensions--en_US
dc.format.accompanyingmaterialNoneen_US
dc.type.degreePh.D.en_US
dc.source.inflibnetINFLIBNETen_US
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File108.97 kBAdobe PDFView/Open
02_certificate & acknowledgements.pdf116.85 kBAdobe PDFView/Open
03_abstract.pdf121.07 kBAdobe PDFView/Open
04_list of publications.pdf112.56 kBAdobe PDFView/Open
05_contents.pdf118.15 kBAdobe PDFView/Open
06_list of tables figures & abbreviations.pdf122.17 kBAdobe PDFView/Open
07_chapter 1.pdf119.55 kBAdobe PDFView/Open
08_chapter 2.pdf133.45 kBAdobe PDFView/Open
09_chapter 3.pdf118.94 kBAdobe PDFView/Open
10_chapter 4.pdf160.11 kBAdobe PDFView/Open
11_chapter 5.pdf527.92 kBAdobe PDFView/Open
12_chapter 6.pdf222.66 kBAdobe PDFView/Open
13_chapter 7.pdf294.72 kBAdobe PDFView/Open
14_chapter 8.pdf1.08 MBAdobe PDFView/Open
15_chapter 9.pdf76.37 kBAdobe PDFView/Open
16_bibliography.pdf116.35 kBAdobe PDFView/Open
17_appendices.pdf299.53 kBAdobe PDFView/Open


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