{"title":"从点云到竣工双节点线框数字孪生:支持自主机器人检测的新方法","authors":"Farzad Azizi Zade, Arvin Ebrahimkhanlou","doi":"10.1007/s43684-024-00082-w","DOIUrl":null,"url":null,"abstract":"<div><p>Previous studies have primarily focused on converting point clouds (PC) into a dense mech of 3D finite element models, neglecting the conversion of PCs into as-built wireframe models with two-node elements for line elements such as beams and columns. This study aims to demonstrate the feasibility of this direct conversion, utilizing building framing patterns to create wireframe models. The study also integrates the OpenSeesPy package for modal analysis and double integration for bending estimation to demonstrate the application of the presented method in robotic inspection. Results indicate the successful conversion of a 4-story mass timber building PC to a 3D structural model with an average error of 7.5% under simplified assumptions. Further, two complex mass timber shed PCs were tested, resulting in detailed wireframe models. According to resource monitoring, our method can process ∼593 points/second, mostly affected by the number of neighbors used in the first stage of sparse points removal. Lastly, our method detects beams, columns, ceilings (floors), and walls with their directions. This research can facilitate various structural modeling directly based on PC data for digital twinning and autonomous robotic inspection.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00082-w.pdf","citationCount":"0","resultStr":"{\"title\":\"Point clouds to as-built two-node wireframe digital twin: a novel method to support autonomous robotic inspection\",\"authors\":\"Farzad Azizi Zade, Arvin Ebrahimkhanlou\",\"doi\":\"10.1007/s43684-024-00082-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Previous studies have primarily focused on converting point clouds (PC) into a dense mech of 3D finite element models, neglecting the conversion of PCs into as-built wireframe models with two-node elements for line elements such as beams and columns. This study aims to demonstrate the feasibility of this direct conversion, utilizing building framing patterns to create wireframe models. The study also integrates the OpenSeesPy package for modal analysis and double integration for bending estimation to demonstrate the application of the presented method in robotic inspection. Results indicate the successful conversion of a 4-story mass timber building PC to a 3D structural model with an average error of 7.5% under simplified assumptions. Further, two complex mass timber shed PCs were tested, resulting in detailed wireframe models. According to resource monitoring, our method can process ∼593 points/second, mostly affected by the number of neighbors used in the first stage of sparse points removal. Lastly, our method detects beams, columns, ceilings (floors), and walls with their directions. This research can facilitate various structural modeling directly based on PC data for digital twinning and autonomous robotic inspection.</p></div>\",\"PeriodicalId\":71187,\"journal\":{\"name\":\"自主智能系统(英文)\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s43684-024-00082-w.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"自主智能系统(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43684-024-00082-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"自主智能系统(英文)","FirstCategoryId":"1093","ListUrlMain":"https://link.springer.com/article/10.1007/s43684-024-00082-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
以往的研究主要集中在将点云(PC)转换为密集的三维有限元模型,而忽略了将 PC 转换为带有双节点元素的线性元素(如梁和柱)的竣工线框模型。本研究旨在证明这种直接转换的可行性,利用建筑框架模式创建线框模型。本研究还集成了用于模态分析的 OpenSeesPy 软件包和用于弯曲估算的双重积分,以演示所提出的方法在机器人检测中的应用。结果表明,在简化假设条件下,成功地将一栋 4 层大规模木结构建筑的 PC 转换为三维结构模型,平均误差为 7.5%。此外,我们还测试了两个复杂的大型木结构建筑 PC,并生成了详细的线框模型。根据资源监测,我们的方法每秒可处理 ∼ 593 个点,这主要受到第一阶段稀疏点去除过程中使用的邻接点数量的影响。最后,我们的方法可以检测梁、柱、天花板(地板)和墙壁及其方向。这项研究有助于直接基于 PC 数据进行各种结构建模,从而实现数字结对和自主机器人检测。
Point clouds to as-built two-node wireframe digital twin: a novel method to support autonomous robotic inspection
Previous studies have primarily focused on converting point clouds (PC) into a dense mech of 3D finite element models, neglecting the conversion of PCs into as-built wireframe models with two-node elements for line elements such as beams and columns. This study aims to demonstrate the feasibility of this direct conversion, utilizing building framing patterns to create wireframe models. The study also integrates the OpenSeesPy package for modal analysis and double integration for bending estimation to demonstrate the application of the presented method in robotic inspection. Results indicate the successful conversion of a 4-story mass timber building PC to a 3D structural model with an average error of 7.5% under simplified assumptions. Further, two complex mass timber shed PCs were tested, resulting in detailed wireframe models. According to resource monitoring, our method can process ∼593 points/second, mostly affected by the number of neighbors used in the first stage of sparse points removal. Lastly, our method detects beams, columns, ceilings (floors), and walls with their directions. This research can facilitate various structural modeling directly based on PC data for digital twinning and autonomous robotic inspection.