Solid Mechanics and Vehicle Conceptual Design

Research on classification of relative density in lunar regolith drilling

  • ZHENG Yanhong ,
  • DENG Xiangjin ,
  • PANG Yong ,
  • JIN Shengyi ,
  • YAO Meng ,
  • ZHAO Zhihui
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  • 1. Beijing Institute of Space System and Engineering, Beijing 100094, China;
    2. Beijing Spacecrafts, Beijing 100094, China

Received date: 2019-08-19

  Revised date: 2019-10-22

  Online published: 2019-10-17

Supported by

China's Lunar Exploration Project

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.

Cite this article

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 . DOI: 10.7527/S1000-6893.2019.23391

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