Information-level real-time AR instruction: a novel dynamic assembly guidance information representation assisting human cognition

In AR assembly, geometric level visualization (GLV) embeds nongeometric attributes as guidance information into the real world and realizes the integration of human intelligence and assembly process at the geometric level. The guidance information generated by GLV is geometric level AR instruction, which contains the product manufacturing information of the object to be assembled. Users need to convert geometric level AR instructions into understandable assembly relationships, so as to understand the assembly tasks to be performed, which greatly reduce the efficiency of users. In fact, users use different logical constraints to organize the corresponding assembly relationships. Logical constraints include fixed constraints and real-time constraints, which reflect the combination of human intelligence and assembly processes at the information level. Among them, fixed constraints are used to represent assembly relationships, and real-time constraints are used to represent hidden manual rules in assembly relationships. These rules are the key information to guide users to establish an assembly relationship. They are usually adaptive information generated by users after understanding the assembly relationship. Although we propose an information-level visualization method (ILV), which can transform fixed constraints into computer-generated graphics, neither GLV nor ILV can use AR instructions to express the manual rules behind assembly relationships. Therefore, we propose an information-level real-time visualization method (IRV), which transforms real-time constraints into computer-generated real-time graphics, called information level real-time AR instruction (IRAI). In a case study, we analyzed and compared the performance of participants under real-time AR instructions with different information levels. This research shows that a high cognitive level of IRAI can make individuals have better performance in work efficiency and cognitive efficiency. This paper mainly studies IRV at different cognitive levels.