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246 results on '"Yuan, Haoran"'

36. Vibrational stability improvement of a mirror system using active mass damping.

Author

He, Shijing, Yuan, Haoran, Wu, Tianyu, Chen, Nuo, Zhang, Xinyu, Wang, Zhizhuo, Liu, Xuerong, and Liu, Fang

Subjects
*ACTIVE noise & vibration control, *STRUCTURAL dynamics, *SUPPLY & demand, *MIRRORS, *VELOCITY
Abstract

Addressing the demand for high stability of beamline instruments at the SHINE facility, a high stability mirror regulating mechanism has been developed for mirror adjustments. Active mass damping was adopted to attenuate pitch angle vibrations of mirrors caused by structural vibrations. An internal absolute velocity feedback was used to reduce the negative impact of spillover effects and to improve performance. The experiment was conducted on a prototype structure of a mirror regulating mechanism, and results showed that the vibration RMS of the pitch angle was effectively attenuated from 47 nrad to 27 nrad above 1 Hz. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Non-Cooperative Spacecraft Pose Estimation Based on Feature Point Distribution Selection Learning.

Author

Yuan, Haoran, Chen, Hanyu, Wu, Junfeng, and Kang, Guohua

Subjects
DEEP learning, SPACE vehicles, MONOCULARS, ALGORITHMS, DETECTORS
Abstract

To address the limitations of inadequate real-time performance and robustness encountered in estimating the pose of non-cooperative spacecraft during on-orbit missions, a novel method of feature point distribution selection learning is proposed. This approach utilizes a non-coplanar key point selection network with uncertainty prediction, pioneering in its capability to accurately estimate the pose of non-cooperative spacecraft, thereby representing a significant advancement in the field. Initially, the feasibility of designing a non-coplanar key point selection network was analyzed based on the influence of sensor layout on the pose measurement. Subsequently, the key point selection network was designed and trained, leveraging images extracted from the spacecraft detection network. The network detected 11 pre-selected key points with distinctive features and was able to accurately predict their uncertainties and relative positional relationships. Upon selection of the key points exhibiting low uncertainty and non-coplanar relative positions, we utilized the EPnP algorithm to achieve accurate pose estimation of the target spacecraft. Our experimental evaluation on the SPEED dataset, which comes from the International Satellite Attitude Estimation Competition, validates the effectiveness of our key point selection network, significantly enhancing estimation accuracy and timeliness compared to other monocular spacecraft attitude estimation methods. This advancement provides robust technological support for spacecraft guidance, control, and proximity operations in orbital service missions. [ABSTRACT FROM AUTHOR]

Published
2024
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