Ontology-based multi-layered robot knowledge framework (OMRKF) for robot intelligence

• Published in: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 429 - 436
• Main Authors: Il Hong Suh, Gi Hyun Lim, Wonil Hwang, Hyowon Suh, Jung-Hwa Choi, Young-Tack Park
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2007
• Subjects: Bidirectional control; Data structures; Grounding; Humans; Intelligent robots; Navigation; Ontologies; Robot sensing systems; USA Councils
• ISBN: 9781424409112; 142440911X
• ISSN: 2153-0858
• DOI: 10.1109/IROS.2007.4399082

An ontology-based multi-layered robot knowledge framework (OMRKF) is proposed to implement robot intelligence to be useful in a robot environment. OMRKF consists of four classes of knowledge (KClass), axioms and two types of rules. Four KClasses including perception, model, activity and context class are organized in a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). The axioms specify the semantics of concepts and relational constraints between ontological elements in each OLayer. One type of rule is designed for relationships between concepts in the same KClasses but in different KLevels. These rules will be used in a way of unidirectional reasoning. And, the other types of rules are also designed for association between concepts in different KLevels and different KClasses to be used in a way of bi-directional reasoning. These features will let OMRKF enable a robot to integrate robot knowledge from levels of sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. To show the validities of our proposed OMRKF, several experimental results will be illustrated, where some queries can be possibly answered by using uni-directional rules as well as bi-directional rules even with partial and uncertain information.