Soon-Jo Chung is Bren Professor of Aerospace in the Division of Engineering and Applied Science (EAS) at Caltech and research scientist at Jet Propulsion Laboratory. He and his team developed a method for using a deep neural network to help autonomous drones “learn” how to land more safely and smoothly.
Tech Briefs: What motivated you to start this project?
Soon-Jo Chung: We talk a lot about artificial intelligence (AI) machinery these days but it is still a challenge when you apply these techniques to complex dynamic physical systems such as drones, biologically inspired robots, or spacecraft. I started on this problem together with Caltech AI experts Anima Anandkumar and Yisong Yue, thanks to the newly created Center for Autonomous Systems and Technologies (CAST) at Caltech.
Tech Briefs: Is there a future for your approach beyond this specific application?
Dr. Chung: Absolutely. We're only focusing on landing because that way we can generate unpredictable wind gusts and turbulence, or what we might call more correctly, ground effects.
Tech Briefs: Will you test in-flight stabilization as well as landing?
Dr. Chung: That's our current work. We need to generate wind gusts and we have the right facility to do so without having to deploy drones outdoors, which takes a lot of time and where the winds would be random.
Tech Briefs: What inputs are fed to the controller for feedback?
Dr. Chung: We have a deep neural network as a feedforward prediction term in the controller. When you train that neural network, the input is basically full spatial information including position; 3D velocity in x, y, z coordinates,;orientation (attitude) of the drone; and angular rate — full spatial information as a function of time. The output is the additional force the drone will need because of the landing and interaction with the ground. It commands the controller to adjust the rotor speeds to provide that force. Since our drone has a quad-rotor system, we can individually control the four different rotor speeds.
Tech Briefs: Are there practical uses for your technique?
Dr. Chung: There are a lot, because all drones have to land at some point; in fact, landing is the trickiest maneuver for any aircraft. That is especially true for any vertical takeoff and landing (VTOL) craft like a quad-rotor drone or a helicopter. Landing presents the greatest challenge because of the ground effects and also the difficulty of perfectly controlling the orientation of the VTOL aircraft due to a second disturbance like sidewinds. So, we are tackling that problem first. But we are also looking at an autonomous flying ambulance that can carry human passengers.
Tech Briefs: When might this be commercialized?
Dr. Chung: Because we have already implemented our software system in actual hardware, pushing it into the commercial domain would take a short amount of time. The only question is to make really sure that our algorithm is scalable and could be translated into the many different types of drones. If there is interest in this kind of technology, I expect that within a couple of years, you will see deep neural network-based control systems applied to commercial drones.
An edited version of this interview appeared in the August Issue of Tech Briefs.