Leaning Impedance Distribution of Object from Images Using Fully Convolutional Neural Networks

Masahiro Kamigaki, Hisayoshi Muramatsu, Seiichiro Katsura

研究成果: Conference contribution

抄録

Robots have been introduced into industrial factory automation. It is necessary to consider interactions between the robots and environments to expand executable tasks of the robots. In the interaction, impedance is an essential factor for the robot to contact with the environment, whereas the impedance is unobservable without contact. In this study, we introduce a concept of affordance for impedance estimation without contact. We propose the impedance estimation method from an RGB image input using deep learning. In this paper, we show that the proposed method can extract pixels corresponding to sponges with its impedance composed of stiffness and viscosity, including the distribution of the impedance. We conducted the experiments to validate the proposed method.

本文言語English
ホスト出版物のタイトルProceedings - IECON 2020
ホスト出版物のサブタイトル46th Annual Conference of the IEEE Industrial Electronics Society
出版社IEEE Computer Society
ページ2662-2667
ページ数6
ISBN(電子版)9781728154145
DOI
出版ステータスPublished - 2020 10 18
イベント46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore
継続期間: 2020 10 192020 10 21

出版物シリーズ

名前IECON Proceedings (Industrial Electronics Conference)
2020-October

Conference

Conference46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
CountrySingapore
CityVirtual, Singapore
Period20/10/1920/10/21

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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