Creating and utilizing symbolic representations of spatial knowledge using mobile robots
| dc.contributor.advisor | Kuipers, Benjamin | en |
| dc.creator | Beeson, Patrick Foil, 1977- | en |
| dc.date.accessioned | 2012-09-04T14:34:42Z | en |
| dc.date.available | 2012-09-04T14:34:42Z | en |
| dc.date.issued | 2008-08 | en |
| dc.description | text | en |
| dc.description.abstract | A map is a description of an environment allowing an agent--a human, or in our case a mobile robot--to plan and perform effective actions. From a single location, an agent’s sensors can not observe the whole structure of a complex, large environment. For this reason, the agent must build a map from observations gathered over time and space. We distinguish between large-scale space, with spatial structure larger than the agent’s sensory horizon, and small-scale space, with structure within the sensory horizon. We propose a factored approach to mobile robot map-building that handles qualitatively different types of uncertainty by combining the strengths of topological and metrical approaches. Our framework is based on a computational model of the human cognitive map; thus it allows robust navigation and communication within several different spatial ontologies. Our approach factors the mapping problem into natural sub-goals: building a metrical representation for local small-scale spaces; finding a topological map that represents the qualitative structure of large-scale space; and (when necessary) constructing a metrical representation for large-scale space using the skeleton provided by the topological map. The core contributions of this thesis are a formal description of the Hybrid Spatial Semantic Hierarchy (HSSH), a framework for both small-scale and large-scale representations of space, and an implementation of the HSSH that allows a robot to ground the largescale concepts of place and path in a metrical model of the local surround. Given metrical models of the robot’s local surround, we argue that places at decision points in the world can be grounded by the use of a primitive called a gateway. Gateways separate different regions in space and have a natural description at intersections and in doorways. We provide an algorithmic definition of gateways, a theory of how they contribute to the description of paths and places, and practical uses of gateways in spatial mapping and learning. | en |
| dc.description.department | Computer Sciences | en |
| dc.format.medium | electronic | en |
| dc.identifier.uri | http://hdl.handle.net/2152/17746 | en |
| dc.language.iso | eng | en |
| dc.rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. | en |
| dc.subject.lcsh | Spatial behavior--Simulation methods | en |
| dc.subject.lcsh | Topology | en |
| dc.subject.lcsh | Artificial intelligence | en |
| dc.subject.lcsh | Mobile robots | en |
| dc.title | Creating and utilizing symbolic representations of spatial knowledge using mobile robots | en |
| thesis.degree.department | Computer Sciences | en |
| thesis.degree.discipline | Computer Sciences | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |