On-orbit refueling is a typical on-orbit service operation, and plays an important role in reducing space transportation costs and mission risks. The visual perception system can perceive the surrounding environment of the operation task, and provide the information to the control system. Nowadays, on-orbit refueling relies on human labor, and is done under the supervision of personnel or by remote operation, lacking autonom. Centered around the future high autonomy on-orbit refueling method based on deep reinforcement learning, this paper conducts research on deep learning based visual perception. Because of the problems of low accuracy of detection of similar instances and being sensitive to changes in illumination, deep learning based methods cannot be directly used for on-orbit refueling. A deep graph reasoning method is proposed for satellite component detection in this paper. The proposed method can effectively detect complex-shaped targets without relying on manually-designed features. It can improve the detection accuracy of parts under complex illumination, and effectively distinguish different parts with similar appearances. Its effectiveness has been verified by mathematical simulation and physical simulation.
CHEN Ao
,
XIE Yongchun
,
WANG Yong
,
LI Linfeng
. Deep graph reasoning method for satellite component detection[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021
, 42(11)
: 525045
-525045
.
DOI: 10.7527/S1000-6893.2021.25045
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