# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
###### 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. knowledge representation; ontology; reasoning; robot; knowledge-based system; applications +
Footnote †: journal: Medicalbiggest challenges in the development of these knowledge-based (KB) robotic systems is to make the programming efforts feasible for combining different types of tasks, robots, and environments. To handle this, knowledge-enabled approaches based on ontology to get the semantics of the environment are adopted. 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. In particular, 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]. In this article, 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. In the domestic environment, an ontology-based knowledge representation shares the concepts between household robots and humans to help the robot to understand the objects in the environment [9]. Domestic environments are indeed dynamic in which robots and human users share the same space [18]. Therefore, ontologies developed [1] for domestic robots are expected to be dynamic and conceptually evolved over time when robots perceive the objects. This is one of the challenging tasks for researchers in this domain. However, it has gained much importance in recent years, and researchers have investigated systematic architectures and algorithms for automatic ontology creation in the domestic environment [19]. Service robots in hospitals require semantic knowledge representation of healthcare objects and their properties to perform a task in an unstructured environment such as tracking object locations in the healthcare system, i.e., for Alzheimer's patients [20], and facilitating elderly people in therapy in hospitals [21]. These tasks are performed by associating semantic knowledge of shared concepts, e.g., objects with an instance in the ontology. The development of ontology-based knowledge representation has also been identified as an emerging field for surgical robotics [22]. Recently, equipping industrial robots with sufficient knowledge has become the subject of attention in the robotics community.