Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning

This paper introduces a novel method for analyzing and relaxing Petri Nets to explain infeasible task plans and to facilitate robust sequential task planning in complex robotic and automated systems. By identifying minimal critical sub-networks causing infeasibility, the approach provides interpretable insights, enabling more adaptive and resilient autonomous operations in dynamic environments. This research offers significant potential for real-world application in robotics, automation, and intelligent manufacturing, where reliable planning and fault diagnosis are crucial for operational efficiency and safety.