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      단순인체모델 기반 휴머노이드의 인간형 전신동작 생성 = Human-like Whole Body Motion Generation of Humanoid Based on Simplified Human Model

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      https://www.riss.kr/link?id=A100234007

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      다국어 초록 (Multilingual Abstract)

      People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.
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      People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented ...

      People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

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      참고문헌 (Reference)

      1 김승수, "인간동작 분석 을 위한 인체모델링" 342-348, 2007

      2 김창환, "모션캡쳐 데이터를 이용한 인간의 상체동작 재현기술" 한국로봇학회 3 (3): 23-32, 2006

      3 C. Atkeson, "Using humanoid robots to study human behavior" 15 (15): 46-56, 2000

      4 S. Ra, "Real-time Adapting Captured Human Motions for Tangible Telemeeting: Kinematic and Dynamic Approaches" 15-17, 2008

      5 Youngjin Choi, "Posture/walking control for humanoid robot based on kinematic resolution of com jacobian with embedded motion" 23 : 1285-1293, 2007

      6 R. Amit, "Parametric Primitives for Motor Representation and Control" 863-868, 2002

      7 K. Nishiwaki, "Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired zmp" 2684-2689, 2002

      8 D. Kim, "Motion-Embedded Cog Jacobian for a Real-Time Humanoid Motion Generation" 14-17, 2005

      9 S. Nakaoka, "Leg motion primitives for a dancing humanoid robot" 610-615, 2004

      10 S. Schaal, "Learning from demonstration" MIT Press 1997

      1 김승수, "인간동작 분석 을 위한 인체모델링" 342-348, 2007

      2 김창환, "모션캡쳐 데이터를 이용한 인간의 상체동작 재현기술" 한국로봇학회 3 (3): 23-32, 2006

      3 C. Atkeson, "Using humanoid robots to study human behavior" 15 (15): 46-56, 2000

      4 S. Ra, "Real-time Adapting Captured Human Motions for Tangible Telemeeting: Kinematic and Dynamic Approaches" 15-17, 2008

      5 Youngjin Choi, "Posture/walking control for humanoid robot based on kinematic resolution of com jacobian with embedded motion" 23 : 1285-1293, 2007

      6 R. Amit, "Parametric Primitives for Motor Representation and Control" 863-868, 2002

      7 K. Nishiwaki, "Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired zmp" 2684-2689, 2002

      8 D. Kim, "Motion-Embedded Cog Jacobian for a Real-Time Humanoid Motion Generation" 14-17, 2005

      9 S. Nakaoka, "Leg motion primitives for a dancing humanoid robot" 610-615, 2004

      10 S. Schaal, "Learning from demonstration" MIT Press 1997

      11 X. Zhao, "Kinematics mapping and similarity evaluation of humanoid motion based on human motion capture" 840-845, 2004

      12 S. Nakaoka, "Generating whole body motions for a biped humanoid robot from captured human dances" 3905-3910, 2003

      13 N. S. Pollard, "Adapting human motion for the control of a humanoid robot" 2 : 1390-1397, 2002

      14 C. Kim, "Adaptation of human motion capture data to humanoid robots for motion imitation using optimization" 13 (13): 377-389, 2006

      15 S. Kagami, "A fast generation method of a dynamically stable humanoid robot trajectory with enhanced zmp constraint" 2000

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2013-01-01 평가 등재후보 1차 FAIL (등재후보1차) KCI등재후보
      2012-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2011-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      2008-09-30 학회명변경 한글명 : 한국로봇공학회 -> 한국로봇학회
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.59 0.59 0.45
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.38 0.31 0.716 0.11
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