The herding algorithm, say the researchers, was inspired by the “Miracle on the Hudson” incident in 2009, when US Airways Flight 1549 struck a flock of geese shortly after takeoff and the pilots were forced to land 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 Bren Scholar in the Division of Engineering and Applied Science as well as a JPL research scientist, 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.”
The new algorithm is designed using a dynamic model of bird flocking based on rules developed by artificial life and computer graphics expert Craig Reynolds, whose Boids simulation program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The “boids” framework is often used in computer graphics to provide realistic-looking representations of flocks of birds, schools of fish, a swarm of insects, or herds of animals.
Herding relies on the ability to manage a flock as a single, contained entity – keeping it together while shifting its direction of travel. The key is that each individual bird in a flock reacts to changes in the behavior of the birds nearest to it.
Effective herding requires an external threat — in this case, the drone — to position itself in such a way that it 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 of the drone, however, has to be precise, say the researchers. If the external threat gets too close or rushes at the flock, the birds will panic and act individually, not collectively.
To teach the drone to herd autonomously, the researchers 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. Their work improves on algorithms designed for herding sheep, which only needed to work in two dimensions, instead of three.
Once they were able to generate a mathematical description of flocking behaviors, the researchers reverse engineered it to see exactly how flocks would respond to approaching external threats, and then used that information to create a new herding algorithm that produces ideal flight paths for incoming drones to move the flock away – without dispersing it – from a protected airspace. They tested the algorithm on a flock of birds near a field in Korea and found that a single drone could keep a flock of dozens of birds out of a designated airspace.
Current strategies for controlling airspace include modifying the surrounding environment to make it less attractive to birds – for example, using trained falcons to scare flocks off – or by using a piloted drone to scare the birds. These strategies can be costly or, in the case of the hand-piloted drone, unreliable, say the researchers.
The effectiveness of the new control algorithm is only limited by the number and size of the incoming birds. Looking ahead, the researchers plan to explore ways to scale the project up for multiple drones dealing with multiple flocks.
For more, see “Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle.”
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