SBIR Phase II: Composing Digital-Twins from Disparate Data Sources

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力涉及到创建一个数字双胞胎，一个复杂工业环境和资产的现实世界系统的交互式三维模型。这种数字孪生体为虚拟现实培训和增强现实实时指导程序在工业工作场所的大规模应用提供了必要的基础设施。通过使这些技术的全面推广成为可能，该技术旨在影响人类健康和安全，提高操作效率，降低过程操作设施（如炼油厂和化工厂）的环境风险。这项技术的长期影响还可以实现自动化和优化，提高效率、安全性和安全性。这些设施是关键的基础设施，在国民经济中发挥着重要作用。该产品的可用性还可以为扫描、空间计算和培训市场中的其他业务增加市场机会。通过将数字孪生生产过程应用于与工业市场无关的新领域，其影响可能会进一步扩大。这个小企业创新研究（SBIR）第二阶段项目正在推进机器学习和空间计算方面的知识和理解。该项目专注于一种将复杂的现实世界系统数字化的方法，以一种有效的方式，足以将捕获的现实重新创建为交互式数字孪生。主要的技术障碍是将描述特定现实世界系统的各个方面的不同数据源组合成一个完整的描述。最初建模的物理系统是工业过程操作，但核心方法可以应用于其他类型的系统，包括自然系统，如雨林。对于工业过程操作，目标是对整个过程操作设施进行编码，在组件级别，以亚厘米级的精度，在当前时间和成本要求的10%。为了实现这一点，该项目将结合物理扫描与工程文档和关系概率。一旦合并，该模型将被用作现实世界系统投影到空间计算环境（如虚拟现实和增强现实）的数字孪生的基础。这些技术用快速扫描、计算机视觉以及程序和训练算法的结合取代了繁琐而有限的静态扫描和密集的人工工作流程。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project relates to the creation of a digital twin, an interactive, 3-dimensional model of a real-world system, of complex industrial environments and assets. This digital twin provides infrastructure necessary for the application of virtual reality training and augmented reality live-guided procedures in industrial workplaces, at scale. By making the full-scale roll-out of these technologies possible, the technology seeks to impact human health and safety, operational efficiencies, and environmental risk reduction for process operations facilities, such as oil refineries and chemical plants. The long-term impacts of this technology may also enable automation and optimization, improving their efficiency, security, and safety. Such facilities are critical infrastructure and play a significant role in the national economy. The availability of this product may also enhance market opportunities for other businesses in the scanning, spatial computing, and training markets. The impact may be further broadened by adapting the process for digital twin production to new domains unrelated to the industrial market.This Small Business Innovation Research (SBIR) Phase II project is advancing knowledge and understanding in both machine learning and spatial computing. This project focuses on a method for digitizing a complex, real-world system, in an efficient manner, sufficient to recreate the captured reality as an interactive digital twin. The primary technical hurdle is the combining of different data sources, that describe aspects of a particular real-world system, into a single, complete description. The initial physical systems being modelled are industrial process operations, but the core methods could apply to other types of systems, including natural systems, such as a rainforest. For industrial process operations, the goal is to encode the entire process operations facilities, at the component level, with sub-centimeter accuracy, at 10% of the current time and cost requirements. To achieve this, this project will combine physical scans with engineering documentation and relational probabilities. Once combined, the model will be used as the basis for a digital twin of the real-world system projected into spatial computed environments, such as virtual and augmented reality. These techniques replace a tedious and limited static scan and intensive human labor workflow with rapid scans, computer vision, and a combination of procedural and trained algorithms.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.