An assembly sequence monitoring method based on workflow modeling for human–robot collaborative assembly

Human–robot collaborative assembly (HRCA) is one of the hot trends in intelligent manufacturing and has gained the attention of many researchers. In HRCA, errors in assembly sequence may reduce working efficiency and damage workpieces. To detect the assembly sequence errors rapidly and not influence the assembly process, the assembly sequence monitoring system demands high real-time performance and non-contact requirements. Therefore, an assembly sequence monitoring method based on workflow modeling is proposed for HRCA. The framework of the monitoring method is divided into an assembly state recognition block, an action library, and a monitoring block. The assembly state recognition block characterizes the complicated and unstructured assembly environment as a state vector in real time. The action library is constructed to model the workflow of the assembly sequences. The monitoring block matches the state-change vector calculated from the state vectors with defined actions in the action library and outputs the monitoring result. The assembly state recognition block is tested in simulation, and the assembly sequence monitoring method is validated in simulation and real-world experiments to exhibit the effectiveness. The comparison with other similar methods illustrates the superiority in monitoring range and recognition accuracy.