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Modelling Spatial Understanding: Using Knowledge Representation to Enable Spatial Awareness in a Robotics Platform
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BibTeX
@MISC{Lochner_modellingspatial,
author = {Martin Lochner and Charlotte Sennersten and Ahsan Morshed and Craig Lindley},
title = {Modelling Spatial Understanding: Using Knowledge Representation to Enable Spatial Awareness in a Robotics Platform},
year = {}
}
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Abstract
Abstract—Robotics in the 21st century will progress from scripted interactions with the physical world, where human programming input is the bottleneck in the robot’s ability to sense, think and act, to a point where the robotic system is able to autonomously generate adaptive representations of its surroundings, and further, to implement decisions regarding this environment. A key factor in this development will be the ability of the robotic platform to understand its physical space. In this paper, we describe a rationale and framework for developing spatial understanding in a robotics platform, using knowledge representation in the form of a hybrid spatial-ontological model of the physical world. While such a system may be implemented with classical ontologies, we discuss the advantages of non-hierarchical modes of knowledge representation, including a conceptual link between information processing ontologies and contemporary cognitive models.
Keyphrases
spatial understanding robotics platform spatial awareness using knowledge representation physical world knowledge representation information processing ontology contemporary cognitive model 21st century human programming input abstract robotics physical space classical ontology robotic system robotic platform robot ability conceptual link key factor non-hierarchical mode hybrid spatial-ontological model adaptive representation scripted interaction
