2016-07-15: Autonomy Incubator Intern Gale Curry Takes Up Robot Flocking Torch

Gale edits her code after a test flight, micro UAV in hand.

The Autonomy Incubator (Ai) first dipped its toes into the world of UAV flocking algorithms last summer, when former intern Gil Montague began researching possibilities for coordinated flight with multiple micro UAVs. This summer, that work continues in the capable hands of Gale Curry.

A Masters student in Mechanical Engineering at Northwestern University, Gale originally studied physics in undergrad until the allure of robotics pulled her away from her theoretical focus.

"I joined the Battle Bots team at UCLA, and that's where I decided I wanted to do more applied things," she said. "I was the social chair."

"The Battle Bots team had a social chair?" I asked.

"Ours did!" she said.

Gale explains the hand-flying pattern she needs to high school intern, Tien Tran.

Gale first became interested in autonomous flocking behaviors during her first term of graduate school, when she took a class on swarm robotics. The idea of modeling robot behavior after that of birds, bees, and ants— small animals that work together to perform complex tasks— has remained inspirational to her throughout her education.

"They're really tiny and pretty simple, but together they can accomplish huge feats," she explained. "I've always liked that in swarm robotics, simpler is better."

Her approach to micro UAV flocking, which she's working on coding right now, is to have one smart "leader" and any number of "followers."  She hopes it will be more efficient and easier to control than developing a flock of equally intelligent, autonomous vehicles and then trying to make them coordinate.

"This way, you focus your time and energy on controlling the one smart one, and the other, 'dumb' ones will follow," she said.

Gale with a member of her fleet.

Gale's goal for her time in the Ai is to have a fleet of these micro UAVs fly through an aperture, like a window, and then reassemble on the other side. Equipping teams of small vehicles with this kind of adaptability and agility in the air, she says, supports the Ai's mission in a variety of ways.

"Flocking couples really easily with the other work here, like the object detection that Deegan is doing or path following, like Javier and Lauren are working on," she said. "It's really applicable to lots of different things."