A robotic chauffeur should reason about spatial information with a variety of scales, dimensions, and ontologies. Rich representations of both the quantitative and qualitative characteristics of space not only enable robust navigation behavior, but also permit natural communication with a human passenger. We apply a hierarchical framework of spatial knowledge inspired by human cognitive abilities, the Hybrid Spatial Semantic Hierarchy, to common navigation tasks: safe motion, localization, map-building, and route planning. We also discuss the straightforward mapping between the variety of ways in which people communicate with a chauffeur and the framework's heterogeneous concepts of spatial knowledge. We present pilot experiments with a virtual chauffeur.

PDF, HTML

In AAAI Spring Symposium Series, Interaction Challenges for Intelligent Assistants 2007. AAAI Technical Report SS-07-04.

@inproceedings{beeson:aaai-sss07, title={Integrating Multiple Representations of Spatial Knowledge for Mapping, Navigation, and Communication}, author={Patrick Beeson and Matt MacMahon and Joseph Modayil and Aniket Murarka and Benjamin Kuipers and Brian Stankiewicz}, booktitle={AAAI Spring Symposium Series, Interaction Challenges for Intelligent Assistants}, note={AAAI Technical Report SS-07-04}, url="http://www.cs.utexas.edu/users/ai-lab?beeson:aaai-sss07", year={2007} }