Kyunam Kim Cited in Cal Tech Magazine

Dr. Kyunam Kim (BEST labber, now a postdoc at CalTech) is on the back cover of the Caltech Magazine (see featured image above). The research developed an algorithm for herding live birds with an autonomous drone, and you can find more details in this article if you are interested:


Engineers at Caltech have developed a new control algorithm that enables a single drone to herd an entire flock of birds away from the airspace of an airport. The algorithm is presented in a study in IEEE Transactions on Robotics.

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 pilots Chesley Sullenberger and Jeffrey Skiles 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.”

“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.

The study, titled “Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle,” was also co-authored by Hyunchul Shim from the Korea Advanced Institute of Science and Technology. Support for the research came from the National Science Foundation.

Abstract of IEEE paper:

In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the m-waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock’s motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.