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dc.contributor.authorFrancis, Gilad
dc.date.accessioned2019-05-24
dc.date.available2019-05-24
dc.date.issued2018-03-13
dc.identifier.urihttp://hdl.handle.net/2123/20443
dc.description.abstractMotion planning is an essential aspect of robot autonomy, and as such it has been studied for decades, producing a wide range of planning methodologies. Path planners are generally categorised as either trajectory optimisers or sampling-based planners. The latter is the predominant planning paradigm as it can resolve a path efficiently while explicitly reasoning about path safety. Yet, with a limited budget, the resulting paths are far from optimal. In contrast, state-of-the-art trajectory optimisers explicitly trade-off between path safety and efficiency to produce locally optimal paths. However, these planners cannot incorporate updates from a partially observed model such as an occupancy map and fail in planning around information gaps caused by incomplete sensor coverage. Autonomous exploration adds another twist to path planning. The objective of exploration is to safely and efficiently traverse through an unknown environment in order to map it. The desired output of such a process is a sequence of paths that efficiently and safely minimise the uncertainty of the map. However, optimising over the entire space of trajectories is computationally intractable. Therefore, most exploration algorithms relax the general formulation by optimising a simpler one, for example finding the single next best view, resulting in suboptimal performance. This thesis investigates methodologies for optimal and safe exploration over continuous paths. Contrary to existing exploration algorithms that break exploration into independent sub-problems of finding goal points and planning safe paths to these points, our holistic approach simultaneously optimises the coupled problems of where and how to explore. Thus, offering a shift in paradigm from next best view to next best path. With exploration defined as an optimisation problem over continuous paths, this thesis explores two different optimisation paradigms; Bayesian and functional.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherFaculty of Engineering and ITen_AU
dc.publisherComputer Scienceen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectRobotic Explorationen_AU
dc.subjectTrajectory Optimizationen_AU
dc.subjectBayesian Optimizationen_AU
dc.subjectFunctional Path Planningen_AU
dc.titleAutonomous Exploration over Continuous Domainsen_AU
dc.typePhD Doctorateen_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU


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