Self-piloted drones have become sophisticated enough to land on moving aircraft carriers, but put a single unexpected tree in the way, and they will crash. Now a five-university group that includes specialists in biology, computer vision and robotics is trying to teach drones to dodge obstacles on the fly. Working with $7.5 million from the Office of Naval Research, the scientists aim to build an autonomous, fixed-wing surveillance drone that can navigate through an unfamiliar city or forest at 35 miles an hour.
The group’s inspiration is the pigeon. Hardy, plentiful and receptive to training, the birds are easy to study. In flight, they estimate the distance between themselves and objects ahead by quickly processing blurry, low-resolution images, just as a drone will need to do. And, crucially, they have a tendency to make decisions at the last moment—within five feet of an obstacle.
The first step is to teach robots to differentiate between obstacles and empty space. Engineers have already figured out how to train point-and-shoot cameras to spot faces in a photo: In a process called supervised learning, a technician feeds millions of images into a computer and tells it to output a “1” when the image contains a human face and a “0” when it does not. But this style of supervised learning would be an impossibly labor-intensive way to train a drone. A human would have to label not just faces but every possible object the robot might encounter. Instead, Yann LeCun, a professor of computer and neural science at New York University who leads the drone’s vision team, is developing software that will allow the drone to draw conclusions about what it’s seeing with much less human coaching. By mimicking the hyperefficient parallel processing method that the brain’s visual cortex uses to classify objects, the software enables features from the raw video frame to be extracted much more quickly. As a result, the drone’s human instructors need to show it only a few hundred to a few thousand examples of each category of object (“car,” “tree,” “grass”) before it can begin to classify those objects on its own.