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dc.contributor.authorNagarajan, Nagamalar
dc.date.accessioned2020-12-09T17:51:53Z
dc.date.available2020-12-09T17:51:53Z
dc.identifier.urihttp://hdl.handle.net/10222/80078
dc.description.abstractCurrently, there are multiple packages available to implement different Simultaneous Localisation And Mapping (SLAM) approaches in Robot Operating System (ROS). To effectively obtain sensor data, these packages use parameters whose values are set from prior knowledge and experience working with robots and SLAM. In this research, using a Multi-Objective Genetic Algorithm (MOGA) to optimise the values for these parameters is proposed. Using MOGA allows trade-offs between the objectives using Pareto dominance technique. Three parameters from the RTAB-Map package are considered for optimisation using three different MOGA mechanisms, Dominance Count, Dominance Rank and Switching Fitness. The quality of the map generated for every set of parameters is taken as the indicator of its performance. The number of corners, number of contours and the proportion of occupied cells in the map are used as quantitative measures of map quality. Finally, results obtained from the algorithm are tested on a Quanser QBot2 robot.en_US
dc.language.isoenen_US
dc.subjectSLAMen_US
dc.subjectRoboticsen_US
dc.subjectGenetic Algorithmen_US
dc.subjectMulti-Objectiveen_US
dc.subjectMappingen_US
dc.titleMulti-Objective Optimisation of RTAB-Map Parameters Using Genetic Algorithm for Indoor 2D SLAMen_US
dc.typeThesisen_US
dc.date.defence2020-11-25
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.external-examinerDr. William Phillipsen_US
dc.contributor.graduate-coordinatorDr. Jacek Ilowen_US
dc.contributor.thesis-readerDr. Kamal El-Sankaryen_US
dc.contributor.thesis-supervisorDr. Jason Guen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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