Stanford’s “autonomous” helicopters teach themselves to fly via “apprenticeship learning”
So the researchers had Oku and other pilots fly entire airshow routines while every movement of the helicopter was recorded. As Oku repeated a maneuver several times, the trajectory of the helicopter inevitably varied slightly with each flight. But the learning algorithms created by Ng’s team were able to discern the ideal trajectory the pilot was seeking. Thus the autonomous helicopter learned to fly the routine better — and more consistently — than Oku himself.
During a flight, some of the necessary instrumentation is mounted on the helicopter, some on the ground. Together, they continuously monitor the position, direction, orientation, velocity, acceleration and spin of the helicopter in several dimensions. A ground-based computer crunches the data, makes quick calculations and beams new flight directions to the helicopter via radio 20 times per second.
The helicopter carries accelerometers, gyroscopes and magnetometers, the latter of which use the Earth’s magnetic field to figure out which way the helicopter is pointed. The exact location of the craft is tracked either by a GPS receiver on the helicopter or by cameras on the ground. (With a larger helicopter, the entire navigation package could be airborne.)