A Robotic Context Query-processing Framework based on Spatio-temporal Context Ontology

Abstract

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-D camera, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on the spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge but also the past. In addition, ST-RCQL includes a variety of time operators and time constants, and thus queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

A Software Architecture for Service Robots Manipulating Objects in Human Environments

“…Model Query Processor handles queries from other components [58]. For example, TMI sends queries to KM about the environment (e.g., poses of static obstacles), object properties (e.g., sizes, weight), and spatial relationships (e.g., objects occluding a target object).…”