STTR Phase I: Semantically-Enabled Augmented Reality for Manufacturing

这个小型企业技术转移（STTR）第一阶段项目促进了更安全、更高效的以人为中心的制造任务。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，并优于现有的制造工作指导系统，从而提高各行业的生产力。这一发展体现了网络人际关系、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。除了其经济意义之外，该项目开发的第一代开放规范现实建模语言（RML）有望在国际标准社区获得广泛接受，从而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更容易访问、搜索和全面注释数据，在用户支持、安全和效率方面开辟新的领域。这个小型企业技术转移（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的人工智能（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，从而消除了单独设计AR交互的需要。该解决方案使培训和工作指导系统具有空间和上下文意识，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，以提高工人的能力，而不是传统的指导工具。RML将衍生并逻辑地描述和计算模拟工厂车间的3D空间场景，之后，RML将作为开放代码库发布给开发人员社区。该系统将实时感知现实世界和物体，在接收输入时进行学习，并优先考虑和呈现AR内容，传达特定于环境的建议和警告。该项目将展示工人、他们的环境和完成任务所使用的工具之间的整合，从而使生产人员能够自信、安全和有效地行动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.