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pro vyhledávání: '"Monica Ekal"'
Publikováno v:
IEEE Robotics and Automation Letters. 7:10946-10953
Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important f
Autor:
Monica Ekal, Rodrigo Ventura
Publikováno v:
Journal of Intelligent & Robotic Systems. 98:153-163
When model-based controllers are used for carrying out precise tasks, the estimation of model parameters is key for a better trajectory tracking performance. We consider the scenario of a free-flying space robot with limited actuation that has graspe
Autor:
Rodrigo Ventura, Monica Ekal
Publikováno v:
ICRA
The performance of model-based motion control for free-flying robots relies on accurate estimation of their parameters. In this work, a method of rigid body inertial parameter estimation which relies on a variational approach is presented. Instead of
Autor:
Monica Ekal, Rodrigo Ventura
Publikováno v:
ICARSC
The estimation of inertial parameters of a robotic system is crucial for better trajectory tracking performance, specially when model-based controllers are used for carrying out precise tasks. In this paper, we consider the scenario of grasping an ob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2844b93f4d12762876f2f8531ca931c8
http://arxiv.org/abs/1802.09338
http://arxiv.org/abs/1802.09338
Autor:
Ekal, Monica, Ventura, Rodrigo
Publikováno v:
Journal of Intelligent & Robotic Systems; Apr2020, Vol. 98 Issue 1, p153-163, 11p