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of 7
pro vyhledávání: '"Szenher, Paul"'
Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation
Externí odkaz:
http://arxiv.org/abs/2403.04936
Publikováno v:
IDETC-CIE 2023
Mobile manipulators have been used for inspection, maintenance and repair tasks over the years, but there are some key limitations. Stability concerns typically require mobile platforms to be large in order to handle far-reaching manipulators, or for
Externí odkaz:
http://arxiv.org/abs/2309.10794
Route planning is essential to mobile robot navigation problems. In recent years, deep reinforcement learning (DRL) has been applied to learning optimal planning policies in stochastic environments without prior knowledge. However, existing works foc
Externí odkaz:
http://arxiv.org/abs/2304.09996
An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the team memb
Externí odkaz:
http://arxiv.org/abs/2210.00867
Autor:
Chen, Fanfei, Szenher, Paul, Huang, Yewei, Wang, Jinkun, Shan, Tixiao, Bai, Shi, Englot, Brendan
This paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and t
Externí odkaz:
http://arxiv.org/abs/2105.04758
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the
Externí odkaz:
http://arxiv.org/abs/2004.05242
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