Abstract
Wearable augmented reality (AR) can superimpose virtual models or annotation on real scenes, and which can be utilized in assembly tasks and resulted in high-efficiency and error-avoided manual operations. Nevertheless, most of existing AR-aided assembly operations are based on the predefined visual instruction step-by-step, lacking scene-aware generation for the assembly assistance. To facilitate a friendly AR-aided assembly process, this paper proposed an Edge Computing driven Scene-aware Intelligent AR Assembly (EC-SIARA) system, and smart and worker-centered assistance is available to provide intuitive visual guidance with less cognitive load. In beginning, the connection between the wearable AR glasses and edge computing system is established, which can alleviate the computation burden for the resource-constraint wearable AR glasses, resulting in a high-efficiency deep learning module for scene awareness during the manual assembly process. And then, based on context understanding of the current assembly status, the corresponding augmented instructions can be triggered accordingly, avoiding the operator’s cognitive load to strictly follow the predefined procedure. Finally, quantitative and qualitative experiments are carried out to evaluate the EC-SIARA system, and experimental results show that the proposed method can realize a worker-center AR assembly process, which can improve the assembly efficiency and reduce the occurrence of assembly errors effectively.
A Model for Complexity Assessment in Manual Assembly Operations Through Predetermined Motion Time Systems