日常生活支援のための机上作業のモデル化およびその認識と支援軌道の生成 Modeling, Recognition and Supporting Trajectory Generation of Daily Object-handling based on Acquired Motion Models

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This paper proposes a robotic assistance system for object handling based on imitative learning. At first, the system learns temporally short segments of motion called"motion primitives"from observation of human object handling tasks. Secondly daily human object-handling is recognized as a sequence of motion primitives. Then the occurrence of an appropriate assisting task defined as a sequence of motion primitives is predicted. Finally the corresponding assisting trajectory is generated from the sequence of motion primitives. The system is composed of such algorithms as object handling motion clustering, human motion recognition, assisting task prediction and trajectory generation, which are learned from human motion. On the other hand, the user specifies the tasks beforehand which the system should support. The validity of the proposed algorithms is confirmed through the experiment of object-handling assistance utilizing a cup.

収録刊行物

  • 日本ロボット学会誌 = Journal of Robotics Society of Japan  

    日本ロボット学会誌 = Journal of Robotics Society of Japan 25(1), 81-91, 2007-01-15 

    The Robotics Society of Japan

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各種コード

  • NII論文ID(NAID)
    10018695546
  • NII書誌ID(NCID)
    AN00141189
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    02891824
  • NDL 記事登録ID
    8635890
  • NDL 雑誌分類
    ZN11(科学技術--機械工学・工業)
  • NDL 請求記号
    Z16-1325
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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