Yang Zhen, Shi Jinlei, Jiang Wenjun, Sui Yuexin, Wu Yimin, Ma Shu, Kang Chunyan, Li Hongting
Department of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Front Psychol. 2019 Jul 24;10:1703. doi: 10.3389/fpsyg.2019.01703. eCollection 2019.
According to the assembly task model proposed by Stork and Schubö (2010), the assembly task is divided into commissioning and joining subtasks. Each subtask includes two sequential stages, namely, perception and response selection, and action. This division enables a convenient discussion of the influences of Augmented reality (AR) assistance on operators during different stages of an assembly task. Research results can provide a basis for the further analysis of the influence mechanism of AR assistance on an assembly task. This study is composed of three experiments. Experiment 1 explores the influences of AR assistance on the performance of the overall assembly task and the commissioning and joining subtasks. Combining a variation of task complexities, Experiments 2 and 3 discuss the influences of AR assistance on the different stages of the commissioning and joining subtasks. We found that AR assistance can shorten the time of the overall assembly task and subtasks (commissioning and joining) and reduce mistakes during these tasks. Moreover, AR assistance can decrease cognitive load in the commissioning subtask, but it increases cognitive load in the joining task with low complexity. In the perception and response selection stage of the commissioning and joining subtasks, AR assistance can shorten the time for users to recognize the target part and understand the assembly relation. This advantage is extremely significant for the high-complexity task. In the action stage of two subtasks, AR assistance can shorten the time for users to capture parts, but it prolongs the time for users to build parts.
根据施托克和舒博于2010年提出的装配任务模型,装配任务分为调试和连接子任务。每个子任务包括两个连续阶段,即感知与反应选择以及行动。这种划分便于讨论增强现实(AR)辅助在装配任务不同阶段对操作人员的影响。研究结果可为进一步分析AR辅助对装配任务的影响机制提供依据。本研究由三个实验组成。实验1探究AR辅助对整体装配任务以及调试和连接子任务绩效的影响。结合不同的任务复杂度,实验2和实验3讨论AR辅助对调试和连接子任务不同阶段的影响。我们发现,AR辅助可缩短整体装配任务和子任务(调试和连接)的时间,并减少这些任务中的错误。此外,AR辅助可降低调试子任务中的认知负荷,但会增加低复杂度连接任务中的认知负荷。在调试和连接子任务的感知与反应选择阶段,AR辅助可缩短用户识别目标零件和理解装配关系的时间。这一优势在高复杂度任务中极为显著。在两个子任务的行动阶段,AR辅助可缩短用户抓取零件的时间,但会延长用户组装零件的时间。