Abstract-A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented. Index Terms-Intelligent service robot, knowledge association, knowledge description, ontology-based knowledge, unified robot knowledge.
For example, Lim et al. developed an ontology-based knowledge framework to include lowlevel knowledge (the perceptions and actions of the robot) and high-level knowledge (the world model) REF .
Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
Computer Science