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孪生模型，一个复杂工业环境和资产的真实世界系统的交互式三维模型。这个数字孪生模型为工业工作场所大规模应用虚拟现实培训和增强现实现场指导程序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，该技术寻求影响人类健康和安全，运营效率以及减少炼油厂和化工厂等工艺操作设施的环境风险。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。这些设施是重要的基础设施，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企业增加市场机会。通过将数字孪生模型的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。这个小型企业创新研究（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。该项目的重点是一种方法，用于数字化一个复杂的，真实世界的系统，以有效的方式，足以重建捕获的现实作为一个互动的数字孪生。主要的技术障碍是将描述特定现实世界系统各个方面的不同数据源组合成一个单一的完整描述。被建模的初始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，目标是在组件级别对整个过程操作设施进行编码，精度为亚厘米，时间和成本要求为当前的10%。为了实现这一点，该项目将结合联合收割机物理扫描与工程文件和相关概率。一旦结合，该模型将被用作投影到空间计算环境（如虚拟现实和增强现实）中的真实世界系统的数字孪生模型的基础。这些技术用快速扫描、计算机视觉以及程序和训练算法的组合取代了繁琐而有限的静态扫描和密集的人力工作流程。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.