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.
MIT’s Robust Robotics Group has developed autonomous-control algorithms for the indoor flight of GPS-denied airplanes. The group developed an algorithm for determining a plane’s “state” — its location, physical orientation, velocity, and acceleration. 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 researchers built their own plane from scratch that 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. Because the problem of autonomous plane navigation in confined spaces is so difficult, the team is initially giving its plane a leg up by providing it with an accurate digital map of its environment. That’s something that the helicopters in the AUVSI challenges don’t have: They have to build a map as they go.
But the plane still has to determine where it is on the map in real time, using data from a laser rangefinder and inertial sensors — accelerometers and gyroscopes — that it carries on board. It also has to deduce its orientation — how much it’s tilted in any direction — its velocity, and its acceleration. Because many of those properties are multidimensional, to determine its state at any moment, the plane has to calculate 15 different values.
To solve the problem, the team combined two different types of state-estimation algorithms. One, called a particle filter, is very accurate but time consuming; the other, called a Kalman filter, is accurate only under certain limiting assumptions, but it’s very efficient. Algorithmically, the trick was to use the particle filter for only those variables that required it and then translate the results back into the language of the Kalman filter.
The MIT researchers’ next step will be to develop algorithms that can build a map of the plane’s environment on the fly.
