Iterative Learning for Periodic Quadrocopter Maneuvers




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Published on Nov 1, 2013

This video demonstrates an iterative learning algorithm that allows accurate trajectory tracking for quadrocopters executing periodic maneuvers.

The algorithm uses measurements from past executions in order to find corrections that lead to better tracking performance. In order to do this, we measure the tracking error over two laps of the maneuver. The new correction is then computed and applied. After waiting for one lap, we begin measuring again and the next learning step follows. For particularly dynamic maneuvers, we begin the learning process at lower execution speeds. This allows us to initially improve performance under safer conditions, and the algorithm provides a means to then transfer the learned corrections from the lower execution speed to higher speeds. The experience gained at lower speeds thus helps us when flying at high speeds, similar to how people learn skills such as martial arts or playing the piano. The method is also applicable to more complex tasks, shown here by the example of the quadrocopter balancing a pole while following a trajectory.

* Researchers
Markus Hehn and Raffaello D'Andrea
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland

* Location
ETH Zurich, Flying Machine Arena - http://www.FlyingMachineArena.org

* Acknowledgments
This work is supported by and builds upon prior contributions by numerous collaborators in the Flying Machine Arena project. See http://www.flyingmachinearena.org/people .
This research was funded in part by the Swiss National Science Foundation (SNSF).


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