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저널 : Sensors, Vol.18, No.10, 3336, 2018. 10.
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논문제목 : “A Robotic Context Query-Processing Framework based on Spatio-Temporal Context Ontology”
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저자 : 이석준, 김인철
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요약 : Service robots operating in indoor environments should recognize dynamic changes from sensors, such as
RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework,
comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing
system (ST-RCQP), for service robots. We designed them based on 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;
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.