A Module-Based Skill Ontology for Industrial Robots

Abstract

With industrial robots ready to take the next step in mastering manufacturing tasks new approaches to reduce the programing effort are needed. This is achieved by introducing skills as robot "know-how" and using them as a higher abstraction level of robot instructions during programming. The skills are reusable items providing motion control and rich declarative descriptions of complex robot capabilities. Storing the skills requires an adequate knowledge representation model that enables reuse and reasoning on skills and simplifies knowledge management. In this thesis we develop a skill representation model and implement it in the LTH knowledge base. It was developed by studying, normalizing, extending and modularizing the current skill concepts in the LTH knowledge base. The developed model is effectively a class hierarchy of the skill concepts implemented in a modularized ontology structure. The resulting model clarifies the intrinsic concepts of a skill and presents a module structure that enables the future development and reuse of skills in general.