Collaborative Research: Correlating Geospatial Data Lineage and Positional Accuracy for Excavation Damage Prevention

合作研究：关联地理空间数据谱系和位置精度以预防开挖损坏

• 批准号: 1265733
• 负责人: Vineet Kamat
• 金额: $ 15万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265733&HistoricalAwards=false
• 关键词: Collaborative; Research; Correlating; Geospatial; Data

The objective of this research is to explore methods that model and quantify the uncertainty associated with recorded underground utility location data. Locational uncertainty is the error associated with the methods of mapping underground utilities, and will be modeled as three-dimensional geometric shapes that correspond to the probability that these shapes contain the utilities. Locational uncertainty is dependent on locational accuracy, which will be estimated from data lineage - the format and process used to collect data. The premise associated with this research is that locational uncertainty results from inaccurate locational information that, in turn, is a function of data lineage. The utility data and its quantified uncertainty will be integrated with excavation operations, and communicated to operators by blending the geometry and attribute information with operators' views of the work environment using video-see-through augmented reality. Field experiments will be conducted with industry partners to validate the proposed geospatial uncertainty model and evaluate its benefits of improving excavation safety.If successful, the research results will lead to improvements in construction operations such as, excavation in the presence of buried utilities, drilling in the presence of hidden obstructions, and robotic construction in the absence of exact prior knowledge of the environment. The project will impact education by promoting subsurface utility engineering as an area of specialization. A diverse group of under-represented students will participate in the project through active collaboration with the Women in Science and Engineering, and the Minority Engineering Program offices at Purdue and the University of Michigan. In summary, the societal benefits of the project are expected to be the improved safety of excavation and other construction operations, and the multi-disciplinary education and training of current and future engineers in an emerging engineering specialty.

本研究的目的是探索对与记录的地下设施位置数据相关的不确定性进行建模和量化的方法。位置不确定性是与绘制地下公用设施的方法相关的误差，并将被建模为与这些形状包含公用设施的概率相对应的三维几何形状。位置不确定性取决于位置准确性，这将根据数据沿袭（用于收集数据的格式和过程）进行估计。与这项研究相关的前提是，位置不确定性是由不准确的位置信息引起的，而位置信息又是数据沿袭的函数。公用事业数据及其量化的不确定性将与挖掘作业相集成，并通过使用视频透视增强现实将几何形状和属性信息与操作员对工作环境的视图混合起来，传达给操作员。将与行业合作伙伴一起进行现场实验，以验证所提出的地理空间不确定性模型并评估其提高开挖安全性的好处。如果成功，研究结果将改善施工作业，例如在埋地公用设施中进行开挖、在隐藏障碍物的存在，以及在缺乏对环境的准确先验知识的情况下进行机器人施工。该项目将通过促进地下公用事业工程作为一个专业领域来影响教育。一群代表性不足的学生将通过与科学与工程领域的女性以及普渡大学和密歇根大学的少数族裔工程项目办公室的积极合作来参与该项目。总之，该项目的社会效益预计将提高挖掘和其他施工作业的安全性，以及对当前和未来工程师进行新兴工程专业的多学科教育和培训。