Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 31 August 2018 / Approved: 31 August 2018 / Online: 31 August 2018 (16:12:54 CEST)

A peer-reviewed article of this Preprint also exists.

Lee, S.; Kim, I. A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology. Sensors 2018, 18, 3336. Lee, S.; Kim, I. A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology. Sensors 2018, 18, 3336.

Journal reference: Sensors 2018, 18, 3336
DOI: 10.3390/s18103336

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.

Subject Areas

intelligent service robot; robotic context query; context ontology

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