Quadrocopters Play Catch With a Stick

February 23rd, 2013 | Robot




Quadrotor Design

The cross-shaped cut-outs are used for easy attachment to the vehicle.

We’ve seen Quadrocopters swarming like space invaders, playing the James Bond theme music, and now, they’re playing catch with a stick.  This new feat was accomplished by Dario Brescianini, a student at ETH Zurich’s Institute for Dynamic Systems and Control, for his Masters Thesis project. The video starts out with one quadrocopter balancing an inverted pendulum on its platform, and then it proceeds to flip the stick over to its friend, who catches it with ease.  It’s really quite remarkable.

Learning the dynamics of the inverted pendulum is commonplace in introductory Engineering courses, but applying it to a flying robot presents a whole new set of challenges.  To complete this task, the team first constructed a 2-D mathematical model of the system to understand at what angles and speed the robot would need to fly in order to catapult the stick toward its partner.  Then, they physically tested their model, made the appropriate adjustments, and restarted the process all over again.  This sort of iterative design can be very effective.  In a sense, it’s a method of fine-tuning the results until you get the perfect outcome.

Quadrotor Shock Absorber

The shock absorber at the end of the pendulum is a balloon filled with flour

Here’s what Markus Hehn (one of Dario’s supervisors) had to say:

“This was a really fun project to work on. We started off with some back-of-the-envelope calculations, wondering whether it would even be physically possible to throw and catch a pendulum. This told us that achieving this maneuver would really push the dynamic capabilities of the system.

As it turned out, it is probably the most challenging task we’ve had our quadrocopters do. With significantly less than one second to measure the pendulum flight and get the catching vehicle in place, it’s the combination of mathematical models with real-time trajectory generation, optimal control, and learning from previous iterations that allowed us to implement this.”

Read more at Robohub.


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