Transit Effort Uses Robots to Gather Inspection Data
Systems engineers are among the growing list of data crunchers attempting to use predictive analytics in transportation applications such as capturing data collected by drones used to inspect the nation’s aging bridges and other infrastructure.
The systems approach combined with automation tools like drones and robots promises to help reduce the cost and downtime associated with traditional methods of inspecting bridges and roadway. The emphasis on big data and predictive analytics also could help planners determine the optimum system design for maximizing traffic capacity, according to university researchers working with a consortium focused on infrastructure issues.
The INSPIRE initiative, which stands for Inspecting and Preserving Infrastructure through Robotic Exploration, looks to develop new ways of maintaining aging U.S. infrastructure as traffic congestion worsens and highway funding declines.
Missouri University of Science and Technology, one of six large U.S. universities participating in the initiative, is focusing on robotic inspections of bridges and roads. The data collected by unmanned systems can be used to prioritize repairs while helping data scientists predict future transportation needs.
Robots are a potentially faster, safer and less expensive way of inspecting bridges and collecting inspection video data, said Ruwen Qin, an associate professor of engineering management and systems engineering at Missouri S&T. Robotic inspections also would allow engineers to shift their focus to decision-making, Qin added.
With that goal in mind, Qin’s team is developing algorithms to process bridge inspection video. The resulting tool would allow users to analyze video to detect and categorize bridge elements, allowing engineers to zero in on specific components in greatest need of repair.
Manual bridge inspections often involve costly, time-consuming operations requiring heavy lifting equipment along with road and bridge closures during inspections. The INSPIRE initiative seeks to help inspectors pinpoint problems faster so they can reduce the down time and cost associated with maintenance.
Ultimately, the effort seeks to reduce costs and improve safety for overextended state and federal highway inspectors. “The impacts are generated from our analytics efforts to better understand, describe, model, design and operate engineered systems,” Qin said.
Other universities participating in the university transportation initiative are City College of New York, Georgia Tech, University of Colorado, and the University of Nevada campuses at Las Vegas and Reno.
Along with the robotics and predictive analytics efforts aimed at mechanical and other systems engineers, the INSPIRE program also will develop a “simulation-based training and control system” to be used to train bridge inspectors.