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Cooperative Quadrocopter Ball Throwing and Catc...
This video shows three quadrocopters cooperatively tossing and catching a ball with the aid of an elastic net. To toss the ball, the quadrocopters accelerate rapidly outward to stretch the net tight between them and launch the ball up. Notice in the video that the quadrocopters are then pulled forcefully inward by the tension in the elastic net, and must rapidly stabilize in order to avoid a collision. Once recovered, the quadrotors cooperatively position the net below the ball in order to catch it. Because they are coupled to each other by the net, the quadrocopters experience complex forces that push the vehicles to the limits of their dynamic capabilities. To exploit the full potential of the vehicles under these circumstances requires several novel algorithms, including: 1) an optimality-based real-time trajectory generation algorithm for the catching maneuver; 2) a time-varying trajectory following control strategy to manage the forces on the individual vehicles that are induced by the net; and 3) learning algorithms that compensate for model inaccuracies when aiming the ball. By Robin Ritz, Mark W. Müller, Markus Hehn, and Raffaello D'Andrea. IDSC, ETH Zürich, Switzerland http://www.flyingmachinearena.org This work is supported by and builds upon prior contributions by past and present FMA collaborators. http://www.idsc.ethz.ch/Research_DAndrea/FMA/participants
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Cooperative Quadrocopter Ball Throwing and Catching - IDSC - ETH Zurich
This video shows three quadrocopters cooperatively tossing and catching a ball with the aid of an elastic net. To toss the ball, the quadrocopters accelerate rapidly outward to stretch the net tight between them and launch the ball up. Notice in the video that the quadrocopters are then pulled forcefully inward by the tension in the elastic net, and must rapidly stabilize in order to avoid a collision. Once recovered, the quadrotors cooperatively position the net below the ball in order to catch it. Because they are coupled to each other by the net, the quadrocopters experience complex forces that push the vehicles to the limits of their dynamic capabilities. To exploit the full potential of the vehicles under these circumstances requires several novel algorithms, including: 1) an optimality-based real-time trajectory generation algorithm for the catching maneuver; 2) a time-varying trajectory following control strategy to manage the forces on the individual vehicles that are induced by the net; and 3) learning algorithms that compensate for model inaccuracies when aiming the ball. By Robin Ritz, Mark W. Müller, Markus Hehn, and Raffaello D'Andrea. IDSC, ETH Zürich, Switzerland http://www.flyingmachinearena.org This work is supported by and builds upon prior contributions by past and present FMA collaborators. http://www.idsc.ethz.ch/Research_DAndrea/FMA/participants
Date: 10/1/12
Views: 14784
Video by:  YouTube
 
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