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Abstract : |
In a large*scale space, structure is at a sig-nificantly larger scale than the observa-tions available at an instant. To learn the structure of a large-scale space from observations, the observer must build a cognitive map of the environment by integrat-ing observations over an extended period of time, inferring spatial structure from perceptions and the effects of actions. The cognitive map representation of large-scale space must account for a mapping, or learning structure from observations, and navigation, or creating and executing a plan to travel from one place to another. Approaches to date tend to be fragile either because they don't build maps; or because they assume nonlocal observations, such as those available in preexist-ing maps or global coordinate systems, including active landmark beacons and geo-iocating satellites. We propose that robust navigation and mapping systems for large.scale space can be developed by adhering to a natural, four-level semantic hierarchy of descriptions for representation, planning, and execution of plans in large-scale space. The four levels are sensorimotor interac-tion, procedural behaviors, topological mapping, and metric mapping. Effective systems represent the environment, rela-tive to sensors, at all four levels and formulate robust system behavior by moving fiexibly between representational levels at run time. We demonstrate our claims in three implemented models: Tour, the Qualnav system simulator, and the NX robot., |
