Ontology-Based AI Planning and Scheduling for Robotic Assembly

The rising demand for customized products necessitates the integration of multiple robotic systems, underscoring the need for advanced production planning and scheduling. This paper introduces an ontology-based, artificial intelligence-enhanced method for dynamic task scheduling and planning, aimed at improving the efficiency of the production process, reducing machine downtime, and consequently increasing throughput in assembly operations. Designed to generate and execute feasible plans dynamically, this method minimizes manual production scheduling and planning efforts. We evaluate its effectiveness using two gear assembly use cases with various robot skills, highlighting its flexibility in production scheduling and its contributions to the evolution of smart manufacturing. The method’s adaptability suggests applicability across diverse smart factory environments.