It’s not easy for human beings to clean up after a hurricane or oil spill. To find out which hazardous chemicals are in an area, or if the air is safe to breathe, disaster response teams risk putting themselves in danger.
So David Lary, a physics professor at the University of Texas at Dallas, is developing a safer cleanup task force.
It’s staffed with robots.
Lary leads a research group called MINTS-AI, which stands for Multi-Scale Integrated Interactive Intelligent Sensing for Actionable Insights. The group is training a fleet of robots that can collect data about the environment all on their own by walking, swimming and flying.
The robots can navigate environmental sites that might be dangerous or challenging for humans to enter, and collect thousands of data points in a matter of minutes. Lary hopes the tech can clue us into our environment without putting people in danger in the process.
Lary’s journey to robot research has been 35 years in the making. After creating a model to study ozone loss in the atmosphere as part of his Ph.D. research at Cambridge University, he hit a roadblock.
How could he analyze multiple different sets of data — for example, temperature, air pressure and humidity — to make one conclusive prediction about the environment?
“Many of these observations don’t necessarily agree with each other,” Lary said. “About 25, 30 years ago … I was looking around earnestly to find a way to deal with this, because it’s a particularly pernicious problem.”
Lary found his answer in machine learning: training computers, or robots, to make those accurate predictions.
Using machine learning to study the environment has advantages when it comes to cost as well as safety. The most accurate sensors to study air and water quality can be large and clunky and cost anywhere from $100,000 to $1,000,000.
Lary’s team acquires sensors that cost around $500 to $10,000, and pairs them with machine learning so that they’re drawing conclusions with the accuracy of a more expensive sensor.
“How do you go from $500 to $100,000?” asked Lakitha Wijeratne, a research associate for the UT Dallas Office of Information Technology and a member of Lary’s lab. “The difference can be made up using machine learning.”
Teaching a robot is a bit like teaching a baby. Just like a parent might show a child a flashcard with a fluffy-eared mammal, and explain it’s called a dog, Lary’s team can teach robots which data points — for example, different air temperatures — correspond to good or bad air quality.
After the teaching phase, a parent might test their child by showing them a picture of a dog, and asking them what it is. Lary’s team can show his robots new temperature levels they haven’t seen before and have the robots make predictions about air quality.
The team can then check the robots’ work against their “flashcard” — in this case, a known answer sheet — to make sure they’re getting it right.
Last year, Lary and his team tested a fleet of air and water robots in Plano to see how good the robots were at collecting accurate data on a body of water they had never encountered before. Their research was published in the journal Sensors last year.
Their flying robot was equipped with several sensors, including a special kind of camera called a hyperspectral imager. While a regular camera takes pictures using three wavelengths of light — red, green and blue — Lary’s hyperspectral camera takes pictures with 462 wavelengths. This means each pixel of the camera’s image has detailed information about the chemical composition of that square of water.
“All of that information is captured in the spectrum of light that reflects from the water,” said John Waczak, a graduate student researcher in Lary’s lab. “That’s like its little chemical thumbprint.”
After giving the aerial robot time to fly over the body of water and get its bearings, Lary’s team released a red substance into the water to “contaminate” it. They then sent the robot to test whether the contaminant would come up in its hyperspectral pictures. It did.
Pratap Tokekar is an assistant professor of computer science at the University of Maryland who was not involved in the UTD research. He said Lary’s use of flying robots to predict measurements that would be slower to collect from the water’s surface is novel.
“The fact that you can make predictions of surface-level measurements from aerial robots is, I think, exciting, and can help scale these systems to larger environments, to monitoring larger bodies of water, and so on,” Tokekar said.
Lary’s team demoed their air and water robots, as well as a walking robot, for their funders at a property in Montague, Texas, this March.
Environmental robots are only a part of Lary’s greater mission of harnessing sensing and machine learning to keep people safe. His group is also working on a set of air sensors that take in data about temperature, pressure, humidity and more to measure air quality at different points in Dallas.
Data from around 30 sensors is already publicly available at the SharedAirDFW website, and Lary’s team has built around 100 more to install around Dallas-Fort Worth.
Lary’s team continues to fine-tune their sensors, figuring out the best ways to get accurate, real-time data on air and water quality. They’re seeking resources and funding to create more air-quality sensors and spread them through the D-FW area. And they’re working on full-body sensors that measure the body’s response to the air around it, in hopes of letting people know in real time what air they’re breathing.
Lary hopes his work can help researchers predict changes in the environment in real time. He compared the impact of his research to the canary in the coal mine, whose death let miners know that the air might not be safe to breathe.
“What I’m trying to work towards, and it’s actually quite a challenging goal, is that no canary has to die,” Lary said. “It’s much better that stuff never hits the fan.”