Application of ε -testers algorithms under sketch calculation model in robot navigation problem
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Application of Ɛ-testers algorithms under sketch and streaming calculation model in robot navigation
WSEAS Transactions on Computers
Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
International Journal of Robotics Research
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
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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 large-scale 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.