Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Ontology-Based Dynamic Context Management and Spatio-Temporal Reasoning for Intelligent Service Robots |
ÀúÀÚ(Author) |
±èÁ¾ÈÆ
À̼®ÁØ
±èµ¿ÇÏ
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Jonghoon Kim
Seokjun Lee
Dongha Kim
Incheol Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 43 NO. 12 PP. 1365 ~ 1375 (2016. 12) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.
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Å°¿öµå(Keyword) |
Áö´ÉÇü¼ºñ½º·Îº¿
»óȲ°ü¸®
½Ã-°ø°£Ãß·Ð
¿ÂÅç·ÎÁö
°è»êÇü¼¼úÀÚ
intelligent service robot
context management
spatio-temporal reasoning
ontology
computable predicate
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