Distributed Iterative Learning Control for a Team of Two Quadrotors

Distributed Iterative Learning Control for a Team of Two Quadrotors

This video shows our distributed iterative learning algorithm in action for a multi-agent system consisting of two quadrotors.

The goal of our work is to enable a team of robots to learn how to accurately track a desired trajectory while holding a given formation. We solve this problem in a distributed manner, where each vehicle has only access to the information of its neighbors and no central control unit is necessary. The desired trajectory is only available to one vehicle. We present a distributed iterative learning control (ILC) approach where the same task is repeated several times. Each vehicle learns from the experience of its own and its neighbors’ previous task repetitions and adapts its feedforward input to improve performance.

This is the first time that multi-agent ILC is implemented on real robots and demonstrated in experiment.

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For theoretic details, check the corresponding paper on arXiv: http://arxiv.org/pdf/1603.05933v1.pdf

Work by Andreas Hock and Angela P. Schoellig at the Dynamic Systems Lab (http://www.dynsyslab.org), University of Toronto Institute for Aerospace Studies (UTIAS), Canada.

This research was supported in part by NSERC grant RGPIN-2014-04634, the Connaught New Researcher Award and the Baden-Württemberg-STIPENDIUM.

Music: Man Of No Ego – Galactic Girl
Source: http://www.ektoplazm.com/free-music/man-of-no-ego-web-of-life
License: https://creativecommons.org/licenses/by-nc-sa/4.0/
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Distributed Iterative Learning Control for a Team of Two Quadrotors



Distributed Iterative Learning Control for a Team of Two Quadrotors