Abstract: Combing ambient intelligence with service robots has shown great potentials in generating radically new system architecture, namely Component-Based Robotic System (CBRS). In order to enable robot to automatically and flexibly utilize service resources in intelligent environment, a novel semantic-quantitative hierarchical service composition method is proposed for supporting complex task accomplishment. A service model with two-layered structure is put forward which incorporates ontology-based service functionality abstraction and state information of individual service resources. A set of unified semantic matching rules are established, based on which a bidirectional breadth-first traversal search algorithm inspired by the maze problem is proposed, which is capable of reliably and dynamically generating plans according to the task requirement. Applications in domestic service robot scenarios are described and experimental results validate the effectiveness of the proposed method.
Keywords: Component-based robotic system, service composition, robot, ambient intelligence, semantic