A distributed knowledge network for real world robot applications

We propose a collaborative knowledge network that we call omniscient spaces in the attempt to generate sophisticated robotic behavior with minimal programming effort. New products are manufactured and brought into our daily life everyday. Robots should need a way to easily integrate new products into their existing recognizable environments. Radio frequency identification gains increasing attention to support context and ambient awareness in dynamically changing environments. To solve robot programming difficulties in our environments, the collaborative knowledge network connects heterogeneous knowledge resources to collectively build up the robot's knowledge required to accomplish a task. Specifically, a decentralized knowledge acquisition and task specific integration model is proposed, where the proposed knowledge integrator merges specific knowledge with existing knowledge into a task requiring knowledge. For this, manufacturers put their product data tailored to plan robot motions online and robots may access the data without authorization. In this work, the best possible scenario under current technological limitations is proposed for real world robot applications. A detailed analysis of the knowledge flow model is described. To verify the validity of the proposed model, a test bed is built and table clearing task is performed according to the distributed knowledge management framework.