Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Capital projects employ engineers to design assets based on project plans. Parts are built in factories all overx000D
the world and shipped to site which in numerous occasions do not fit to the existing infrastructure. These andx000D
other factors cost the capital project industry $1.6 Trillion annually. Using a revolutionary and comprehensivex000D
end-to-end software solution to solve the problem of industrial rework and facilitate proactive, cost effectivex000D
project execution digital project information generated via the collection of a variety of sensory and spatial datax000D
to create a Digital Twin of the physical project is currently being used. However, data collection, typicallyx000D
performed manually, requires to be automated to enable effective capital project execution while maintainingx000D
the required sensor data accuracy.x000D
By identifying mismatches between reality and plan, the automated sensor data collection will enable correctivex000D
actions early in the project execution before they cause rework.x000D
The focus of this project is to automate capital project fast data collection in high definition via the use ofx000D
autonomous Unmanned Ground Vehicles (UGVs).x000D
In particular we will develop enhanced robot's obstacle avoidance mechanisms to better estimate sensorx000D
placement and maneuvering the UGV to optimally position the sensors for relevant and accurate datax000D
collection. A vision input (LIDAR sensor) will be used as a feedback to servo the UGV for 3D motionx000D
purposes while having the capabilities to quickly identify precise sensor placement. The focus is on developingx000D
tools for (1) accurate detection of complex obstacles typically found in commercial sites (e.g., overhandx000D
cables), (2) UGV path planning for optimal sensor placement via visual servoing/surveying, and (3) analyze thex000D
effectiveness of the use of UGVs in construction sites.