Integrating hybrid diagnostic reasoning in plan execution monitoring for cognitive factories with multiple robots

For reliable and fault tolerant operation of cognitive factories, we introduce an algorithm to monitor plan executions. According to this algorithm, when some changes or discrepancies are detected, appropriate decisions are given based on the causes of these changes or discrepancies. To identify these causes (e.g., broken robots or robot components), we introduce a novel diagnostic reasoning method which synergistically integrates hypothetical reasoning, geometric reasoning, and learning from earlier experiences. Based on these causes, if necessary, new hybrid plans (task plans integrated with feasibility checks) are computed to reach the manufacturing goals by allowing repairs of robots/components. The results of our experiments over reasonably-sized cognitive factory scenarios show the usefulness of (i) diagnostic reasoning for execution monitoring, (ii) allowing repair actions during replanning, and (iii) learning from experiences. We provide a video of dynamic simulation of our execution monitoring algorithm with Kuka youBots and a Nao humanoid robot as the supplementary material.