Worcester Polytechnic Institute (WPI) researcher Raghvendra Cowlagi has been awarded $530,029 by the National Science Foundation (NSF) to determine strategies that could sift through large amounts of sensor data to improve the operations of autonomous systems.
The three-year project will use mathematics to address the problem of identifying the most useful information in large data sets for automated decision making. Cowlagi, associate professor in the Department of Aerospace Engineering, said the work could have applications for traffic management, aerial parcel delivery, deployment of scarce resources during emergencies, and responses to natural disasters or industrial accidents.
“This is about trying to access the most useful information that may be contained in massive sets of data collected by everything from traffic cameras to social media accounts,” Cowlagi said. “We want to find the most relevant data, fuse together data from diverse sources, and enable automated systems to reach decisions with fewer, but more meaningful, pieces of information.”
Sensors provide information—sometimes, too much information—about an environment. Although video from unmanned aerial vehicles (UAVs) and social media posts could provide information about a road network during flooding, too much video footage and too many tweets could overwhelm the computing power of an automated system that is trying to rapidly determine the best routes for ambulances that are responding to the disaster.
Cowlagi, whose research focuses on how autonomous vehicles understand their environments and make independent decisions, will develop mathematical models to configure sensors according to an identified objective. He will test the models using publicly available Air Force Research Laboratory data collected by cameras, seismic and acoustic sensors, and radar. In addition, Cowlagi will conduct indoor and outdoor experiments at WPI using wheeled robots on the ground and miniature UAVs equipped with cameras.
Two graduate students will work on the project. Cowlagi also will supervise teams of undergraduate students who will work on experimental validation aspects of the project.
“This project has real-world applications that could impact many people,” Cowlagi said. “It could help those who want to use drones to safely deliver packages in a neighborhood or those who want to plan for the optimal distribution of medical resources during a crisis. We can’t simply try to build bigger and more powerful computers. We need to find ways to deliberately collect the most relevant information in order to make faster, automated decisions.”
