Spatio-temporal prediction of soil deformation in bucket excavation using machine learning

Yuki Saku, Masanori Aizawa, Takeshi Ooi, Genya Ishigami

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a prediction model for three-dimensional spatio-temporal soil deformation in bucket excavation. The prediction model for soil deformation (PMSD) consists of two machine learning processes: the long short-term memory (LSTM) and convolutional autoencoder (Conv-AE). These processes use datasets obtained from an experimental apparatus for bucket excavation developed in this work. The apparatus equips multiple depth cameras that precisely capture time-series data of soil deformation in bucket excavation. The LSTM, an extension of a recurrent neural network, successively predicts three-dimensional soil deformation. The Conv-AE is incorporated to both ends of the LSTM in order to quasi-reversibly compress and reconstruct the datasets so that the computational burden of the LSTM is relaxed. Qualitative and quantitative evaluations of the PMSD confirm the feasibility of time-series prediction of three-dimensional soil deformation. The Conv-AE shows sufficient accuracy equivalent to the measurement accuracy of the depth camera. The prediction accuracy of the PMSD is about 10 mm in most of the cases.

Original languageEnglish
JournalAdvanced Robotics
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Bucket excavation
  • LSTM
  • machine learning
  • soil deformation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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