Robust object-mass measurement using condition-based less-error data selection for large-scale hydraulic manipulators

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

A practical framework for measuring the mass of an object grasped by the end-effector of a large-scale hydraulic manipulator, such as construction manipulators, is proposed. Such a measurement system requires high accuracy and robustness considering the nonlinearity and uncertainty in hydraulic pressure-based force measurement. It is thus difficult to precisely model the system behaviors and completely remove error force components, so our framework adopts a less-error data selection approach to improving the reliability of the measurand. It first detects the on-load state to extract reliable data for mass measurement, including evaluating measurement conditions to omit sensors in indeterminate conditions and redefining three-valued outputs such as on, off, or not determinate, to improve robustness, then extracts data during the object-grasp state identified by the grasp motion model and removes high-frequency component by a simple low-pass filter, to improve accuracy, and finally integrates date from plural sensors using the posture-based priority and averages all selected data, to improve reliability. Evaluation experiments were conducted using an instrumented hydraulic arm. Results indicate that our framework can precisely measures the mass of the grasped object in various detection conditions with less errors.

元の言語English
ホスト出版物のタイトル2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1679-1684
ページ数6
ISBN(電子版)9781479973965
DOI
出版物ステータスPublished - 2014 4 20
イベント2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014 - Bali, Indonesia
継続期間: 2014 12 52014 12 10

Other

Other2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014
Indonesia
Bali
期間14/12/514/12/10

    フィンガープリント

ASJC Scopus subject areas

  • Biotechnology
  • Artificial Intelligence
  • Human-Computer Interaction

これを引用

Kamezaki, M., Iwata, H., & Sugano, S. (2014). Robust object-mass measurement using condition-based less-error data selection for large-scale hydraulic manipulators. : 2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014 (pp. 1679-1684). [7090576] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO.2014.7090576