MIT News: Algorithm Developed For Determining Trajectory For Robot Planes Without GPS

A small, autonomous helicopter, programmed by MIT students under the direction of Professor Nick Roy, passes through a simulated window as part of a competition held over the summer. (2009 Pic)
Credit: Nicholas Roy

Autonomous robotic plane flies indoors

For decades, academic and industry researchers have been working on control algorithms for autonomous helicopters — robotic helicopters that pilot themselves, rather than requiring remote human guidance. Dozens of research teams have competed in a series of autonomous-helicopter challenges posed by the Association for Unmanned Vehicle Systems International (AUVSI); progress has been so rapid that the last two challenges have involved indoor navigation without the use of GPS.

But MIT’s Robust Robotics Group — which fielded the team that won the last AUVSI contest — has set itself an even tougher challenge: developing autonomous-control algorithms for the indoor flight of GPS-denied airplanes. At the 2011 International Conference on Robotics and Automation (ICRA), a team of researchers from the group described an algorithm for calculating a plane’s trajectory; in 2012, at the same conference, they presented an algorithm for determining its “state” — its location, physical orientation, velocity and acceleration. Now, the MIT researchers have completed a series of flight tests in which an autonomous robotic plane running their state-estimation algorithm successfully threaded its way among pillars in the parking garage under MIT’s Stata Center.

“The reason that we switched from the helicopter to the fixed-wing vehicle is that the fixed-wing vehicle is a more complicated and interesting problem, but also that it has a much longer flight time,” says Nick Roy, an associate professor of aeronautics and astronautics and head of the Robust Robotics Group. “The helicopter is working very hard just to keep itself in the air, and we wanted to be able to fly longer distances for longer periods of time.”

With the plane, the problem is more complicated because “it’s going much faster, and it can’t do arbitrary motions,” Roy says. “They can’t go sideways, they can’t hover, they have a stall speed.”

Found in translation

To buy a little extra time for their algorithms to execute, and to ensure maneuverability in close quarters, the MIT researchers built their own plane from scratch. Adam Bry, a graduate student in the Department of Aeronautics and Astronautics and lead author on both ICRA papers, consulted with AeroAstro professor Mark Drela about the plane’s design. “He’s a guy who can design you a complete airplane in 10 minutes,” Bry says. “He probably doesn’t remember that he did it.” The plane that resulted has unusually short and broad wings, which allow it to fly at relatively low speeds and make tight turns but still afford it the cargo capacity to carry the electronics that run the researchers’ algorithms.

MIT News: Guiding Autonomous Robot Planes With Simple Hand Gestures

CAMBRIDGE, Mass. -- Aircraft-carrier crew use a set of standard hand gestures to guide planes on the carrier deck. But as robot planes are increasingly used for routine air missions, researchers at MIT are working on a system that would enable them to follow the same types of gestures.

The problem of interpreting hand signals has two distinct parts. The first is simply inferring the body pose of the signaler from a digital image: Are the hands up or down, the elbows in or out? The second is determining which specific gesture is depicted in a series of images. The MIT researchers are chiefly concerned with the second problem; they present their solution in the March issue of the journal ACM Transactions on Interactive Intelligent Systems. But to test their approach, they also had to address the first problem, which they did in work presented at last year’s IEEE International Conference on Automatic Face and Gesture Recognition.

Yale Song, a PhD student in MIT’s Department of Electrical Engineering and Computer Science, his advisor, computer science professor Randall Davis, and David Demirdjian, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), recorded a series of videos in which several different people performed a set of 24 gestures commonly used by aircraft-carrier deck personnel. In order to test their gesture-identification system, they first had to determine the body pose of each subject in each frame of video. “These days you can just easily use off-the-shelf Kinect or many other drivers,” Song says, referring to the popular Microsoft Xbox device that allows players to control video games using gestures. But that wasn’t true when the MIT researchers began their project; to make things even more complicated, their algorithms had to infer not only body position but also the shapes of the subjects’ hands.

The MIT researchers’ software represented the contents of each frame of video using only a few variables: three-dimensional data about the positions of the elbows and wrists, and whether the hands were open or closed, the thumbs up or down. The database in which the researchers stored sequences of such abstract representations was the subject of last year’s paper. For the new paper, they used that database to train their gesture-classification algorithm.