Abstract
Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.
Highlights
Ontology-based knowledge representation is significantly important for autonomous robots [1]
We focused on a systematic review of the ontology-based KB systems of autonomous social robots that are working in domestic, hospital, and industrial sectors
It is expected that under the auspices of artificial intelligence, semantic web technology, and other accompanying ideas or visions, the development of this field with real-world robotic applications will continue to advance. This survey was intended to show the recent developments in ontology-based knowledge representation systems for robotics that can lay the groundwork to inspire future research in this area
Summary
Ontology-based knowledge representation is significantly important for autonomous robots [1]. Within the realm of autonomous social robotic systems [3], the demand for knowledge representation about the objects and the environment using ontologies [4] to improve the semantic understanding of the task has become a primary concern. Knowledge representation in these robotic systems through ontologies defines the link between individual instances and describes their roles in the domain [5]. The advantage of ontology-based approaches is that knowledge pieces, which are independent of the robot, task, and environment, can be shared between different robots and applied to a variety of robotic applications. The potential benefit of ontology-based semantic knowledge representation can be obtained using web-based services for knowledge sharing among different robots to perform the tasks such as RoboEarth [13], KnowRob [14], openEASE [15], RoboBrain [16], and RoboCSE [17]
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