Caltech engineers use UAS to herd birds away from airspace at airports
Engineers at the California Institute of Technology (Caltech) have developed a new control algorithm that allows a single UAS to herd an entire flock of birds away from the airspace of an airport.
The project was inspired by the 2009 "Miracle on the Hudson," when US Airways Flight 1549 struck a flock of geese shortly after takeoff and forced pilots to land the plane in the Hudson River off Manhattan.
“The passengers on Flight 1549 were only saved because the pilots were so skilled,” says Soon-Jo Chung, an associate professor of aerospace, and the principal investigator on the drone herding project.
“It made me think that next time might not have such a happy ending. So I started looking into ways to protect airspace from birds by leveraging my research areas in autonomy and robotics.”
Currently, methods for controlling airspace include modifying the surrounding environment to make it less attractive to birds, using trained falcons to scare flocks off, and even piloting a UAS to scare the birds. These strategies can be costly, and in the case of the hand-piloted UAS, it can be unreliable, Chung says.
“When herding birds away from an airspace, you have to be very careful in how you position your drone,” Chung explains. “If it's too far away, it won't move the flock. And if it gets too close, you risk scattering the flock and making it completely uncontrollable. That's difficult to do with a piloted drone.”
Herding relies on the ability to manage a flock as a single, contained entity, so that it stays together while shifting its direction of travel. Each bird in a flock reacts to changes in the behavior of the birds nearest to it.
Therefore, effective herding requires an external threat to position itself in a way that encourages birds along the edge of a flock to make course changes that then affect the birds nearest to them, who affect birds farther into the flock, and so on, until the entire flock changes course. The positioning has to be precise, because if the external threat gets too zealous and rushes at the flock, the birds will panic and act individually, not collectively.
To teach the UAS to herd autonomously, Chung and his colleagues studied and derived a “mathematical model of flocking dynamics” to describe how flocks build and maintain formations, how they respond to threats along the edge of the flock, and how they then communicate that threat through the flock.
The work of the Caltech team improves on algorithms designed for herding sheep, which only needed to work in two dimensions, as opposed to three.
“We carefully studied flock dynamics and interaction between flocks and pursuers to develop a mathematically sound herding algorithm that ensures safe relocation of flocks using autonomous drones,” says Kyunam Kim, postdoctoral scholar in aerospace at Caltech and a co-author of the IEEE paper where the algorithm is presented in a study.
Once researchers generated a mathematical description of flocking behaviors, they “reverse engineered it to see exactly how approaching external threats would be responded to by flocks.” They used that information to create a new herding algorithm that produces ideal flight paths for incoming UAS to move the flock away from a protected airspace without dispersing it.
“My previous research focused on spacecraft and drone swarms, which turned out to be surprisingly relevant for this project,” Chung notes.
The Caltech team tested the algorithm on a flock of birds near a field in Korea. During testing, they found that one UAS could keep a flock of dozens of birds out of a designated airspace.
Chung notes that “the effectiveness of the algorithm is only limited by the number and size of the incoming birds,” and adds that the team plans to explore ways to scale the project up for multiple UAS dealing with multiple flocks.
The algorithm is presented in a study called “Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle,” which can be read here.