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pro vyhledávání: '"Sarabakha, Andriy"'
Autor:
Qiao, Zhongzheng, Pham, Xuan Huy, Ramasamy, Savitha, Jiang, Xudong, Kayacan, Erdal, Sarabakha, Andriy
In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study i
Externí odkaz:
http://arxiv.org/abs/2405.01054
Autor:
Sarabakha, Andriy
The off-the-shelf drones are simple to operate and easy to maintain aerial systems. However, due to proprietary flight software, these drones usually do not provide any open-source interface which can enable them for autonomous flight in research or
Externí odkaz:
http://arxiv.org/abs/2303.01813
In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perc
Externí odkaz:
http://arxiv.org/abs/2207.14131
In this work we present a platform to assess robot platform skills using an internet-of-things (IoT) task board device to aggregate performances across remote sites. We demonstrate a concept for a modular, scale-able device and web dashboard enabling
Externí odkaz:
http://arxiv.org/abs/2201.09565
Drone racing is a recreational sport in which the goal is to pass through a sequence of gates in a minimum amount of time while avoiding collisions. In autonomous drone racing, one must accomplish this task by flying fully autonomously in an unknown
Externí odkaz:
http://arxiv.org/abs/2008.02596
Autor:
Sarabakha, Andriy, Kayacan, Erdal
This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled
Externí odkaz:
http://arxiv.org/abs/1905.10796
In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties)
Externí odkaz:
http://arxiv.org/abs/1904.00249
Autor:
Yezerska, Khrystyna, Dushina, Anastasia, Sarabakha, Andriy, Wagner, Peter, Dyck, Alexander, Wark, Michael
Publikováno v:
In International Journal of Hydrogen Energy 8 August 2022 47(68):29495-29504
Akademický článek
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Publikováno v:
In Applied Soft Computing Journal August 2019 81