Integration of Stochastic Petri Nets and Ontologies for Human–Robot Interaction Control Systems in Collaborative Assembly Tasks

Abstract This study introduces an integrated control framework that combines stochastic Petri nets with ontology-based knowledge representation to improve safety and efficiency during collaborative human–robot assembly. The objective is to endow a robotic coworker with the ability to reason about uncertain human actions while respecting semantic task constraints. The method maps ontology concepts, relations, and individuals to places, transitions, and tokens of a stochastic Petri net, yielding a unified state-space model governed by probabilistic firing rates and logical axioms. Procedures include formal reachability analysis for deadlock and hazard detection, synthesis of an optimal task-allocation policy that maximizes a cumulative reward balancing speed and safety, and implementation of the entire model in the Webots simulator. Experimental investigations were carried out on a pick-and-place assembly scenario involving a six-degree-of-freedom manipulator and a human operator across 100 simulation trials. The proposed controller achieved a 27% reduction in average assembly time and a 42% decrease in unsafe proximity events relative to a time-triggered baseline, while eliminating all deadlock states identified in the reachability graph. The results demonstrate that the semantic–probabilistic integration provides a viable foundation for next-generation collaborative robot controllers capable of adapting to variable human behavior without compromising safety or productivity.