Autonomous Mobile Asphalt Density Profiling Robot to Reduce Worker Risk
Loading...
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Autonomous Mobile Asphalt Density Profiling Robot to Reduce Worker Risk
Alternative title
Published Date
2023-06
Publisher
Center for Transportation Studies, University of Minnesota
Type
Report
Abstract
MnDOT pavement construction personnel have lately improved quality assurance (QA) through the use of nondestructive air coupled ground penetrating radar sensors. Although proving to be accurate, the acquisition process can be manually intensive and hazardous especially when deployed adjacent to prevailing traffic. The primary objective of this project was to deliver to MnDOT two low-cost, modular, highly transportable, mobile robot platforms designed specifically for pavement density profile testing. Several field tests were performed to assess feasibility of the platform under different operational scenarios. Modularity was ensured by integrating separate, distributed, plug-and-play modules that could be reused for other mobile platforms, should the need arise for future implementations. By implementing two robots, the transferability of the architecture was demonstrated. The mobile robotic platforms were purposely assembled from widely available, low-cost, commercial, off-the-shelf components to minimize overall cost, recognizing that the landscape for such platforms has been evolving rapidly.
Description
Related to
Replaces
License
Collections
Series/Report Number
;CTS 23-04
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Morris, Ted; Papanikolopoulos, Nikolaos. (2023). Autonomous Mobile Asphalt Density Profiling Robot to Reduce Worker Risk. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/257422.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.