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

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.