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.

这个小型企业技术转移（STTR）I期项目促进了更安全，更有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，并胜过现有的制造业工作指导系统，从而提高整个行业的生产力。这一发展体现了网络人际关系，数字双胞胎和智能工厂应用的出现，增强了美国制造业领导力，增强经济竞争力并加强了国家安全。预期的商业平台即服务（PAAS）解决方案有望使大约10,000家美国制造公司受益。除经济影响外，该项目中开发的第一代，开放式的现实建模语言（RML）有望在国际标准界获得广泛的接受，从而改善了各种行业垂直行业的空间系统自动化。最终，该系统将使物理世界更容易访问，可搜索和全面注释，并在用户支持，安全性和效率方面解锁新的前沿。 这个小型企业技术转移（STTR）I阶段项目应对在制造环境中充分利用增强现实（AR）工具的关键任务挑战。它利用本体结构化的数据和专有的人工智能（AI）驱动的知识系统，用于自动化3D空间中特定于上下文特定的AR内容，从而消除了对单独设计的AR交互的需求。该解决方案使培训和工作指导系统能够在空间和上下文上意识到，以适应影响工人安全和效率的动态条件。该项目的目的是证明和量化自动生成，空间和语言意识的AR如何提供工作指导，机器状态数据和危害警告，以提高工人功能与常规指导工具。 RML将得出并在逻辑上描述和计算代码模拟工厂地板的3D空间场景，后来，RML将作为开发人员社区的开放代码库发布。该系统将实时感知现实世界和对象，在接收输入时学习，并确定和渲染AR内容，以传达特定于上下文的建议和警告。该项目将证明工人，环境和所涉及的工具之间的整合以完成其任务，以便生产人员可以安全，安全，有效地行事。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估的支持。