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Knowledge-enabled decision making for robotic system utilizing ambient service components


Robotic and ambient intelligent environments are much more demanding regarding the knowledge a robot needs to have. A skilled robot performs human-scale manipulation tasks, interacts with a variety of objects, understands instructions given by humans and most importantly, requires the capability of interpreting ubiquitous resources and assembling them into a complex plan. In this paper, a novel Knowledge-enable Decision Making Framework (KDMF) is proposed for Component-Based Robotic System (CBRS). We exploit that the use of domain knowledge is pivotal to endow robots with higher degrees of autonomy and intelligence in CBRS. Ontology knowledge about classes, properties, relations is organized in OWL based conceptual map, which allows automated inference to derive new pieces of information. Knowledge about tasks is specified in a tree data structure, and knowledge about components' functions is formulated by a specific type of service specification profile. Using the knowledge representation, a task-oriented decision making method is proposed that integrates knowledge inference and service components utilization. In practical applications of ambient and robotic assisted living, robot's plans generated by the decision making software are based on the knowledge of components, rather than particular device instances, which improves the reusability and flexibility of the system. Experimental results validate the effectiveness of the proposed method.