Knowledge Based Hierarchical Decomposition of Industry 4.0 Robotic Automation Tasks

Robotic automation has made significant inroads into industrial manufacturing and supply chains. With Industry 4.0 requirements proposing further autonomy to robotic participants, it is necessary to reason about robotic tasks within a knowledge dependent software framework. In this work, we model robotic automation tasks using hierarchical decomposition models, that are used to extract action plans to satisfy end goals. By abstracting components as intelligent agents that have perception, action, goal and knowledge base elements, we provide a reusable model to abstract robotic automation behavior. Through the use of the formal typed specification language Orc, implementations that confirm to the goal decomposition process are formulated. We demonstrate our techniques over Smart Warehouse deployments, with domain specific ontologies to ensure accurate type based descriptions of knowledge elements. This provides a generic framework for deploying intelligent automation systems across a host of industrial settings.