Toward Efficient Robot Teach-In and Semantic Process Descriptions for Small Lot Sizes
Alexander Perzylo, Nikhil Somani, Stefan Profanter, Markus Rickert, Alois Knoll · 2015
Abstract—We present a novel robot programming methodology that is aimed at reducing the level of robotics expert knowledge needed to operate industrial robotic systems by explicitly mod-eling this knowledge and abstracting it from the user. Most of the current robot programming paradigms are either user-centric and fully-specify the robot’s task to the lowest detail (used mostly in large industrial robotic systems) or fully autonomous solutions that generate the tasks from a problem description (used often in service and personal robotics). We present an approach that is user-centric and can interpret underspecified robot tasks. Such task descriptions make the system amenable for users that are experts in a particular domain, but have limited knowledge about robotics and are thus not able to specify low-level details and instructions. Semantic models for all involved entities enable automatic reasoning about underspecified tasks and missing pieces of information. We demonstrate this approach on an industrial assembly use-case and present a preliminary evaluation—both qualitatively and quantitatively—vis-à-vis state-of-the-art solutions available from industrial robot manufacturers. I.