STTR Phase I: Semantically-Enabled Augmented Reality for Manufacturing

STTR 第一阶段：用于制造的语义增强现实

• 批准号: 2335533
• 负责人: Tony Hodgson
• 金额: $ 27.5万
• 依托单位: ETHAR, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335533&HistoricalAwards=false
• 关键词: STTR; Phase; Semantically; Enabled; Augmented

This Small Business Technology Transfer (STTR) Phase I project facilitates safer and more efficient human-centered manufacturing tasks. The introduction of context-sensitive work guidance through immersive technologies will expedite workforce training, enhance users' spatial awareness, and outperform existing manufacturing work instruction systems, leading to heightened productivity across industries. This development embodies the emergence of cyber-human relationships and Digital Twin and Smart Factory applications, reinforcing U.S. manufacturing leadership, bolstering economic competitiveness, and fortifying national security. The anticipated commercial Platform-as-a-Service (PaaS) solution is poised to benefit approximately 10,000 U.S. manufacturing firms. Beyond its economic implications, the first-generation, open-specification Reality Modeling Language (RML) developed in this project is expected to gain widespread acceptance in the international standards community, improving spatial system automation across diverse industry verticals. Ultimately, this system will render the physical world more accessible, searchable, and comprehensively annotated with data, unlocking new frontiers in user support, safety, and efficiency. This Small Business Technology Transfer (STTR) Phase I project addresses mission-critical challenges for fully leveraging Augmented Reality (AR) tools in manufacturing environments. It draws upon ontologically structured data and a proprietary Artificial Intelligence (AI)-driven knowledge system for automating the generation and display of context-specific AR content in 3D space, eliminating the need for individually designed AR interactions. The solution enables training and work instruction systems to become spatially- and contextually aware, in order to adapt to dynamic conditions impacting worker safety and efficiency. The objective of this project is to demonstrate and quantify how automatically generated, spatially- and semantically aware AR can provide work guidance, machine status data, and hazard warnings to increase worker capabilities versus conventional guidance tools. The RML will be derived and logically describe and computationally code the 3D spatial scene of a simulated factory floor, and later, RML will be released as an open code library to the developer community. The system will sense the real world and objects in real-time, learn as input is received, and prioritize and render AR content communicating context-specific suggestions and warnings. This project will demonstrate integration between workers, their environment, and the tools engaged to complete their tasks so production personnel can act confidently, safely and effectively.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这一小型企业技术转让(STTR)第一阶段项目促进了更安全、更高效的以人为本的制造任务。通过沉浸式技术引入上下文敏感的工作指导将加快劳动力培训，增强用户的空间意识，并超越现有的制造工作指导系统，从而提高各行业的生产率。这一发展体现了网络与人类关系以及数字孪生和智能工厂应用的出现，加强了美国制造业的领导地位，增强了经济竞争力，并加强了国家安全。预期的商业平台即服务(PaaS)解决方案将使大约10,000家美国制造公司受益。除了其经济影响外，在该项目中开发的第一代开放规范现实建模语言(RML)预计将在国际标准界获得广泛接受，改善不同行业垂直领域的空间系统自动化。最终，该系统将使物理世界变得更容易访问、更可搜索，并使用数据进行全面注释，从而在用户支持、安全和效率方面开启新的前沿。该小型企业技术转移(STTR)第一阶段项目解决了在制造环境中充分利用增强现实(AR)工具的关键任务挑战。它利用本体结构化数据和专有人工智能(AI)驱动的知识系统，在3D空间中自动生成和显示特定于上下文的AR内容，消除了单独设计AR交互的需要。该解决方案使培训和工作指导系统能够在空间和背景上感知，以适应影响工人安全和效率的动态条件。该项目的目标是演示和量化自动生成的、空间和语义感知的AR如何提供工作指导、机器状态数据和危险警告，以提高员工的能力，而不是传统的指导工具。RML将被派生出来，并对模拟工厂地板的3D空间场景进行逻辑描述和计算编码，随后，RML将作为开放代码库发布给开发人员社区。该系统将实时感知真实世界和对象，在收到输入时学习，并优先处理和呈现AR内容，传达特定于上下文的建议和警告。这个项目将展示工人、他们的环境和完成任务所使用的工具之间的集成，从而使生产人员能够自信、安全和有效地行动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。