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