Exploring augmented reality for worker assistance versus training
Exploring augmented reality for worker assistance versus training
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DOI：
10.1016/j.aei.2021.101410
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发表时间：
2021-09-09
影响因子：
8.8
通讯作者：
Marsella, Stacy C.
中科院分区：
文献类型：
--
作者：
Moghaddam, Mohsen;Wilson, Nicholas C.;Marsella, Stacy C.
本文旨在提高对增强现实（AR）作为一种基于工作场所的学习和培训技术的能力的基本理解，以支持涉及复杂操作和推理的手动或半自动化制造任务。本研究通过20名参与者参与的被试间实验室实验，对现实生活中的机电装配任务进行了研究，以研究通过增强现实技术传递信息的不同模式与传统训练方法相比，对任务效率、错误数量、学习、独立性和认知负荷的影响。AR应用程序是在Unity中开发的，并部署在HoloLens 2头显上。我们还对来自行业和学术界的专家进行了访谈，以对增强现实作为制造业工人的培训工具与辅助工具的可行性以及对智能机制的需求产生新的见解，这些机制能够使工人与增强现实之间进行自适应和个性化的互动。研究结果表明，尽管增强现实组与对照组在任务完成时间、学习曲线和独立性方面的表现相当，与传统指令相比，AR显著减少了错误数量，传统指令在移除AR支持后仍能维持。从实验和专家访谈中得出的一些见解被讨论，以告知未来AR技术的设计，以培训和协助现任和未来的制造工人进行复杂的操作和推理任务。
This paper aims at advancing the fundamental understanding of the affordances of Augmented Reality (AR) as a workplace-based learning and training technology in supporting manual or semi-automated manufacturing tasks that involve both complex manipulation and reasoning. Between-subject laboratory experiments involving 20 participants are conducted on a real-life electro-mechanical assembly task to investigate the impacts of various modes of information delivery through AR compared to traditional training methods on task efficiency, number of errors, learning, independence, and cognitive load. The AR application is developed in Unity and deployed on HoloLens 2 headsets. Interviews with experts from industry and academia are also conducted to create new insights into the affordances of AR as a training versus assistive tool for manufacturing workers, as well as the need for intelligent mechanisms that enable adaptive and personalized interactions between workers and AR. The findings indicate that despite comparable performance between the AR and control groups in terms of task completion time, learning curve, and independence from instructions, AR dramatically decreases the number of errors compared to traditional instruction, which is sustained after the AR support is removed. Several insights drawn from the experiments and expert interviews are discussed to inform the design of future AR technologies for both training and assisting incumbent and future manufacturing workers on complex manipulation and reasoning tasks.