月球风化层钻取采样过程密实度分类研究

doi:10.7527/S1000-6893.2019.23391

Abstract

Abstract: Drilling is an important approach to acquire samples from lunar regolith. The relative density is an in situ characteristic of lunar regolith and will influence the strategy of drilling process. According to the working principle of drilling, the perception scheme of lunar soil relative density is proposed with the instantaneous information derived from sensors of force, velocity, current, and temperature. Based on the deep learning method, a class of gated recurrent unit neural network is constructed for variable length data sequence. The network can achieve the online classification of relative density of lunar soil. The delay time for relative density perception is about 33 s and the correct ratio is higher than 89.36% to unknown data sequence, indicating that the method has high classification precision and generality.

Key words: moon, drilling, lunar soil relative density, recurrent neural network, online classification

CLC Number:

V447⁺.1

ZHENG Yanhong, DENG Xiangjin, PANG Yong, JIN Shengyi, YAO Meng, ZHAO Zhihui. Research on classification of relative density in lunar regolith drilling[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(4): 223391-223391.

References 24

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Research on classification of relative density in lunar regolith drilling

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