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

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力在于，拟议的创新研究有可能发展成为具有垂直机动性的独立机器人检测工具，该工具携带RGB-D相机和探地雷达（GPR），以检测和表征表面缺陷和地下缺陷。软件算法和功能将集成到这个爬墙机器人中，以实现数据收集和分析过程的自动化，特别是在人类操作员难以进入的关键位置。机器人检测工具的使用将使人工建造的混凝土结构的评估和状态健康监测能够更快、更彻底地进行，并且通过消除脚手架和阻塞交通的需要，以更低的成本进行。它还将提高检查的安全性和速度，从而导致更频繁和按需检查，从而使国家基础设施（桥梁，隧道，水坝，建筑物）更加安全。这个小企业创新研究（SBIR）第一阶段项目的重点是开发创新的方法和软件算法，用于地下缺陷的3D GPR成像，基于视觉的精确定位和表面缺陷检测，表征和绘图。软件功能将集成到该爬壁机器人中，以评估其性能并验证创新的可行性。该项目的智能优势包括3D探地雷达成像方法，该方法将机器人控制和基于视觉的精确定位与探地雷达信号处理相结合，可以定位混凝土结构中的地下缺陷和嵌入（钢筋、管道、裂缝、空隙、分层等），这将彻底改变探地雷达数据的收集、解释和显示方式。该方法使GPR-Rover能够以任意和不规则的轨迹扫描表面，而不是沿着网格线移动，以定位地下目标并发现分层区域。提出的机器人视觉检测和机器学习算法不仅可以检测和表征表面缺陷，而且可以在三维地图上精确地注册缺陷，从而更好地定位和可视化。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 I project is that the proposed innovative research has the potential to be developed into a self-contained robotic inspection tool with vertical mobility that carries an RGB-D camera and ground penetrating radar (GPR) to detect and characterize both surface flaws and subsurface defects. The software algorithms and functions will be integrated into this wall-climbing robot to automate the data collection and analysis process, especially at critical locations that are difficult to access by human operators. The use of the robotic inspection tool will allow the evaluation and condition health monitoring of human-built concrete structures to be performed significantly faster, more thoroughly and at a lower cost by eliminating the need for scaffolding and blocking traffic. It will also improve inspection safety and speed which leads to more frequent and on-demand inspections, thus making the national infrastructure (bridges, tunnels, dams, buildings) more secure. This Small Business Innovation Research (SBIR) Phase I project focuses on developing innovative methods and software algorithms for 3D GPR imaging of subsurface defects, vision-based accurate positioning and surface flaw detection, characterization and mapping. The software functions will be integrated into this wall-climbing robot to evaluate the performance and validate the feasibility of the innovation. The intellectual merit of this project includes the 3D GPR imaging method that combines robot control and vision-based accurate positioning with GPR signal processing to locate the subsurface defects and embedment (rebar, pipes, fractures, voids, delamination, etc.) in concrete structures that will revolutionize the way GPR data is collected, interpreted and displayed. This method enables the GPR-Rover to scan the surface in arbitrary and irregular trajectory rather than move along grid lines to locate subsurface targets and discover the areas of delamination. The proposed robotic visual inspection and machine learning algorithm is novel because it can not only detect and characterize surface flaws but also precisely register them on 3D map for better localization and visualization.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.