The broader impact/commercial potential of this I-Corps project is that the technology (robotic hardware and data analytic software) has the potential to be developed into a new product as a complete, self-contained robotic inspection tool that carries a RGB-depth camera and Ground Penetrating Radar (GPR) sensor with vertical mobility for detecting surface flaws and sub-surface defects. The use of the tool will allow the inspection of human-built structures to be performed significantly faster, more thoroughly and at a lower cost by eliminating the need for scaffolding or blocking traffic. It will also improve inspection safety and speed which leads to more frequent and on-demand inspections, thus making the national infrastructure more secure.

This I-Corps project has the following innovations: 1) novel design of robotic inspection system carrying a RGB-depth camera to detect surface flaws, and a Ground Penetrating Radar (GPR) sensor to detect sub-surface defects on both ground and vertical surfaces that other robots cannot do; 2) a novel method that enable the robot to scan the surface in an arbitrary trajectory with accurate positioning and integrate the control with GPR signal processing to identify areas of delamination and locate subsurface embedment (rebar, pipes, fractures, voids, delamination, etc.) in concrete structures that will revolutionize the way GPR data is interpreted and displayed. The value proposition of the robotic inspection system includes: 1) automating the vision/GPR data collection process with minimal human intervention; 2) simplifying the robot control to scan the surface in an arbitrary trajectory; 3) making inspection results easy to understand by non-professionals; 4) and improving the inspection speed and accuracy.

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.

Project Start
Project End
Budget Start
2019-04-15
Budget End
2020-09-30
Support Year
Fiscal Year
2019
Total Cost
$50,000
Indirect Cost
Name
CUNY City College
Department
Type
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10031