A Method for Learning a Petri Net Model Based on Region Theory

The deployment of robots in real life applications is growing. For better control and analysis of robots, modeling and learning are the hot topics in the field. This paper proposes a method for learning a Petri net model from the limited attempts of robots. The method can supplement the information getting from robot system and then derive an accurate Petri net based on region theory accordingly. We take the building block world as an example to illustrate the presented method and prove the rationality of the method by two theorems. Moreover, the method described in this paper has been implemented by a program and tested on a set of examples. The results of experiments show that our algorithm is feasible and effective

Virtual reality and process mining applied to operator training in complex assembly tasks

One of the proposals of Industry 4.0 is the integration of machines and operators through network connections and information management. One of the challenges that can be addressed following this approach is the management of knowledge in the industry or, in other words, the transmission of knowledge from the expert operators to the new ones. This work has been developed a system that combines Virtual Reality and Process Mining to allow this knowledge transmission in the particular case of assembly tasks. Virtual Reality allows to create work environments that reproduce the real ones and interact with them without detracting resources of other processes or facing the risks of working with real systems. Process Mining allows to acquire the knowledge of experts, store it in models and then transmit it to novices. The developed system has been tested by means of several examples of assemblies with Lego