The Gridswarms project, on which my friend Renzo is working, is about creating swarms of miniature helicopters that fly around and share their computing resources in order to perform tasks in a coordinated manner, inspired by the way they do it on the Discovery Channel. (That is, inspired by cooperation among insects. Nothing else.)
Yes, it sounds pretty science fiction, and yes, it's been covered several times by the media, e.g. Wired. And I'm pretty sure they will succeed as well.
I'm not officially part of that project, but as I tend to hang out with Renzo all the time, we cooperate quite a bit on our research as well. Only on things we both understand, of course; I hardly know anything about hardware design, Kalman filters and such stuff. One problem I could help him with, though, is the automatic design of the basic helicopter controller.
You see, the plan is to have the helicopters perform all sorts of interesting flocking and swarming behaviour, perhaps coordinated target tracking, or distributed surveillance. But these advanced behaviours need to be built on top of a reliable flight controller layer, whose task it is to keep the machine flying in the face of various disturbances, and implement the orders (e.g. "go to point x, y, z, but quickly!") of the swarming layers.
Designing such a controller by hand is in no way easy. It is even harder than actually flying the helicopter, which I can tell you is not easy at all. So we turned to evolutionary algorithms and neural networks to do the job for us.
Now, what an evolutionary algorithm does is essentially systematical trial-and-error on a massive scale. Doing massive trial and error on real helicopters (albeit miniature) would be a bit... expensive. Not to mention slow. So we needed to do the evolution in simulation, and because the real helicopters are still under developments we had to contend with a third-party (very detailed) helicopter simulation for the time being.
Without further ado, this is what it looks like. Remember that we have provided absolutely no knowledge of how to fly the helicopter to the neural network, it has learnt it all by itself, through evolution!
As you might or might not see from the movie, the task is to fly a trajectory defined by a number of waypoints (yellow). (As should be evident from the movie, the helicopter is shown from behind in the right panel and from above in the left panel.) Our evolved neural networks perform this task much better than a hand-coded PID controller, and is reliable even under various disturbances, such as changing wind conditions. And as far as we know, we are the first people in the world to successfully evolve helicopter control.
Getting there wasn't all that easy, though. To begin with, the computational complexity is absolutely massive - we used a cluster of 34 Linux computers, and even then a typical evolutionary run (a hundred generations or so) took several hours. What we also discovered was that no matter how much computer power you have, the right behaviour won't evolve if the structure of the neural network is wrong. It took us a lot of time to find out, but a standard fully connected MLP net won't cut it. Instead you have modularize either according to the dimensions of control or according to...
...but hey, I'm losing you. I can't get too far into technical details on a blog, can I? Go read the paper we presented at CEC two weeks ago instead. It is available online, click here!
Anyway, there is more work to do on the project, and I'll get back to this topic!
Tuesday, August 01, 2006
Learning to fly
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