SBIR Phase II: Use of Robotic Inspection and Data Analytics to Localize and Visualize the Structural Defects of Civil Infrastructure

SBIR 第二阶段：使用机器人检查和数据分析来定位和可视化民用基础设施的结构缺陷

• 批准号: 2112199
• 负责人: Yang He
• 金额: $ 86.68万
• 依托单位: InnovBot LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112199&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Use; Robotic

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project seeks to produce two robotic systems for ground survey applications and inspection of vertical surfaces to detect and visualize both surface flaws and subsurface embedment in concrete structures using a camera and a ground penetrating radar (GPR). The instrumentation may reduce the time, difficulty, and cost to layout gridlines in civil infrastructure and make it possible to automate data collection at difficult-to-access locations with minimum human intervention. The use of the robotic inspection systems may allow the evaluation and inspection tasks to be performed faster, more thoroughly, with minimal safety risks to workers, and at a lower cost by eliminating the need for scaffolding and blocking traffic. The proposed research and development may advance the state-of-the-art robotic technology and produce climbing machines, vision-based accurate positioning, and 3D Ground Penetrating Radar (GPR) imaging software as a holistic solution to improve the way GPR data is collected, interpreted, and visualized. The solution can be extended to other non-destructive testing (NDT) applications and may help make the national infrastructure (e.g., bridges, tunnels, dams, and buildings) more secure. This Small Business Innovation Research (SBIR) Phase II project seeks to improve the accuracy and robustness of vision-based positioning and 3D GPR imaging algorithms while developing new software functions and integrating them into two robotic systems deployed to inspect both ground surfaces (e.g., bridge decks and highway pavement) and vertical surfaces (e.g., building facades and bridge foundations). The intellectual merit of this project is the automated 3D GPR data collection and imaging method that combines robotic control and vision-based accurate positioning with GPR signal processing for locating the subsurface defects and embedment (rebar, pipes, fractures, voids, delamination, etc.) in concrete structures. This method enables the robots to scan the surfaces in arbitrary and irregular trajectories rather than along gridlines to locate subsurface targets and discover the areas of delamination. Tagging the GPR measurements with accurate position information in a synchronized way at each sampling step enables high-resolution 3D GPR imaging.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.

该小企业创新研究 (SBIR) 第二阶段项目具有更广泛的影响/商业潜力，旨在生产两种用于地面测量应用和垂直表面检查的机器人系统，以使用相机和图像来检测和可视化混凝土结构中的表面缺陷和地下嵌入探地雷达（GPR）。该仪器可以减少在民用基础设施中布局网格线的时间、难度和成本，并可以在难以访问的位置以最少的人为干预自动收集数据。使用机器人检查系统可以使评估和检查任务更快、更彻底地执行，对工人的安全风险最小，并且通过消除脚手架和阻塞交通来降低成本。拟议的研究和开发可能会推进最先进的机器人技术，并生产攀爬机、基于视觉的精确定位和 3D 探地雷达 (GPR) 成像软件，作为改进探地雷达数据收集方式的整体解决方案、解释和可视化。该解决方案可以扩展到其他无损检测 (NDT) 应用，并可能有助于提高国家基础设施（例如桥梁、隧道、水坝和建筑物）的安全性。该小型企业创新研究 (SBIR) 第二阶段项目旨在提高基于视觉的定位和 3D GPR 成像算法的准确性和鲁棒性，同时开发新的软件功能并将其集成到部署用于检查两个地面（例如，桥梁）的两个机器人系统中。甲板和高速公路路面）和垂直表面（例如建筑外墙和桥梁基础）。该项目的智力优势是自动化 3D GPR 数据采集和成像方法，将机器人控制和基于视觉的精确定位与 GPR 信号处理相结合，用于定位地下缺陷和嵌入（钢筋、管道、裂缝、空隙、分层等）。 ）在混凝土结构中。这种方法使机器人能够以任意和不规则的轨迹而不是沿着网格线扫描表面，以定位地下目标并发现分层区域。在每个采样步骤以同步方式用准确的位置信息标记探地雷达测量结果，可实现高分辨率 3D 探地雷达成像。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing

• DOI: 10.1109/jsen.2022.3164707
• 发表时间: 2023-03
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Jinglun Feng;Liang Yang;Ejup Hoxha;Jizhong Xiao