STTR Phase I: Intelligent Instruction Systems using Augmented Reality

STTR 第一阶段：使用增强现实的智能教学系统

• 批准号: 0512610
• 负责人: Jayfus Doswell
• 金额: --
• 依托单位: Juxtopia, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0512610&HistoricalAwards=false
• 关键词: STTR; Phase; Intelligent; Instruction; Systems

This Small Business Technology Transfer Phase I project seeks to enhance the current state of-the-art in production line manufacturing training processes with an initial focus on the automotive industry. Human-directed training by other skilled employees and supervisors does not take advantage of recent technologies that can make training routines more autonomous, thereby mitigating the impact on the quality of training in periods where resources are constrained. Empirical training results (i.e., quantitative training feedback that is not currently available) can assist planners in optimizing manufacturing processes. The proposed innovation lies in the creation of intelligent instruction systems that exploit adaptive software mechanisms (i.e., intelligent software agents) and augmented reality (AR) techniques, thus enhancing training techniques while promoting continuous process standardization, assessment, optimization and resource management. Since it is common that production-line employees are required to wear goggles, intelligent agents could transfer their instruction via goggle-like wearable computers (i.e., AR) that overlay the actual visual field with text and computer graphics. A major aspect of this research is the architecture, framework, and feasibility analysis of the insertion of these technologies into real manufacturing environments. Initial analysis will be with two facilities, General Motors and BMW. The proposed techniques will enable real-time assessment of employees during training routines and enable the software agents to automatically and proactively reinforce weaker areas based on these assessments. An overall assessment model of all employees can characterize the entire workforce for a particular facility. This overall assessment can be used to enhance resource management required by the on-going problem of absenteeism. Probably the greatest innovation would be a framework to allow planners to exploit the assessments and optimize manufacturing processes by refactoring traditional, perhaps obsolete, production processes. If intelligent agents could manage and direct the cross training of employees in any industry, a major pay-off would be the efficient use of resources. In addition, the use of such agents would enhance the following as expressed by the common claim: "Standardization of processes results in the predictability of the final product". If all employees were trained to perform similar tasks consistently and intelligent agents and AR could enforce that standardization, quality would then also become predictable and measurable. Manufacturing environments beyond automotive will have the models, frameworks, tools, and techniques to standardize their processes. As a result, the quality of products delivered to external customers in any industry can be improved, while at the same time production costs can be reduced.

这个小型企业技术转让第一阶段项目旨在提高生产线制造培训过程的当前最先进水平，最初的重点是汽车工业。由其他熟练员工和主管进行的以人为本的培训没有利用可以使培训程序更加自主的最新技术，从而在资源有限的时期减轻对培训质量的影响。经验性培训结果（即，目前无法获得的定量培训反馈）可以帮助计划人员优化制造过程。提出的创新在于创建智能教学系统，利用自适应软件机制（即智能软件代理）和增强现实（AR）技术，从而提高培训技术，同时促进持续的流程标准化、评估、优化和资源管理。由于生产线员工通常需要戴上护目镜，智能代理可以通过类似护目镜的可穿戴计算机（即AR）传递指令，该计算机将文本和计算机图形覆盖在实际的视野上。本研究的一个主要方面是将这些技术引入实际制造环境的体系结构、框架和可行性分析。初步分析将在通用汽车和宝马两家公司进行。所提出的技术将能够在培训过程中对员工进行实时评估，并使软件代理能够根据这些评估自动主动地加强薄弱环节。所有员工的整体评估模型可以描述特定设施的整个劳动力。这种全面评估可用于加强对持续存在的缺勤问题所需的资源管理。最大的创新可能是一个框架，允许计划人员通过重构传统的（可能过时的）生产过程来利用评估和优化制造过程。如果智能代理可以管理和指导任何行业员工的交叉培训，那么主要的回报将是资源的有效利用。此外，如共同的索赔要求所表达的那样，使用这种剂将加强以下方面：“过程的标准化导致最终产品的可预测性”。如果所有员工都经过培训，能够始终如一地执行类似的任务，智能代理和AR可以强制执行这种标准化，那么质量也将变得可预测和可衡量。汽车以外的制造环境将有模型、框架、工具和技术来标准化它们的过程。因此，可以提高任何行业交付给外部客户的产品质量，同时可以降低生产成本。