Modelling Spatial Understanding: Using Knowledge Representation to Enable Spatial Awareness in a Robotics Platform

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. Further, we describe the proposed CogOnto (cognitive ontology) model, which enables symbol grounding for a cognitive computing system, using sensor data gathered from diverse and heterogeneous sources, associated with humanly crafted symbolic descriptors. 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.