<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brandon J DeHart</style></author><author><style face="normal" font="default" size="100%">Dana Kulic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying Balance Capabilities using Momentum Gain</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE-RAS Int. Conf. on Humanoid Robotics (HUMANOIDS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/8246928/</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">561-568</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	The ability of a legged system to balance depends&amp;nbsp;on both the control strategy used and the system’s physical&amp;nbsp;design. To quantify a system's inherent balance capabilities, we define&amp;nbsp;momentum gains&amp;nbsp;for general&amp;nbsp;2D and 3D models. We provide two methods for calculating&amp;nbsp;these gains, and relate both velocity and momentum gains to the&amp;nbsp;centroidal momentum of a system, a commonly used measure of&amp;nbsp;aggregate system behaviour. Finally, we compare velocity and&amp;nbsp;momentum gains as criteria for the design of simple balancing&amp;nbsp;systems using a parameterized optimization framework.
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