例３：外界センサー
人間
 モーションキャプチャ：
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運動データ
 床半力
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外力
逆運動学計算
例３：外界センサー
人間
動作画像
モーションキャプチャー
逆運動学
逆力学
モーションキャプチャー
例３：外界センサー
人間
15 リンク
[Nakamura et al. 2000] ,
[Venture et al. 2008]
34 自由度(DOF)
マーカーから逆運動学計算
→関節角度、速度、加速度 [Yamane
et al. 2003]
例３：外界センサー
人間モデリング・最少パラメータ
 Standard parameters = full set of inertial parameters
 Base parameters = identifiable parameters from
inverse dynamics only (YB of full rank)
15
同定のため適当な動作は？
 リグレサーの状件数
Cross Validation
例３：外界センサー
人間・RT同定
 モーションキャプチャ
(motion analysis)
 床半力 (Kistler)
 Screen for visual
feedback
 PC for motion
acquisition
 PC for real-time
computation
例３：外界センサー
人間・RT同定
From motion capture (RT IK computations)
and force-plates (RT generalized effort computations)
base and SP are identified with RT method
(recursive algorithm)
Geometric scaling and initial SP
Measure the geometric parameters of the
model from motion capture: automatic
scaling from marker positions
Estimate the initial SP f ref from geometric
parameters and database of human body.
Initial geometric parameters and
SP identification for 3 candidates
with 3 different morphologies:
1.73m 58Kg, 1.62m 54Kg and
1.76m 76.3Kg
Persistent Exciting trajectories
Using RT color changes to specify the links
not yet completely identified, results are
obtained in a shorter time with more
accuracy.
3. Real time identification of the human dynamics
Persistent Exciting trajectories
同定結果
Comparison of some identified parameters
with literature [Young et al. 1983]
応用：スポーツトレーニング
 33 year old female
 Stanton marathon training program in preparation
for the 2009 Tokyo Marathon.
 Training program = 16 week program
 5-days a week running schedule
 Gradually increased running distance from 20km/week to
80km/week in the peak 13th week, then tapering in the
final 3 weeks before the race.
 Prior to that subject runs 25km/week
 Record on a weekly basis / several sessions omitted
応用：スポーツトレーニング
例４：関節粘弾性の同定
 Biomechanics developed methodologies for
measuring various dynamics properties of the
human body
Determination of standard values of joints’ visco-elastic
properties based on well calibrated measuring equipments
and averaging of data of many subjects.
 Equipment need mechanical stiffness and accuracy
⇒ heaviness and bulkiness (dynamometer)
⇒ not applicable to everyone, specially to people under
rehabilitation and medical treatments.

⇒ System to measure patient-specific visco-elastic
properties of limb joints without pain and constraints
highly required.
例４：関節粘弾性の同定
Joint modelling
Model of visco-elastic
properties adapted
from biomechanics
most popular model
+Identification of human joint passive dynamics, Proc. of the IEEE Int. Conf. on Robotics and Automation, pp
2960-2965, 2006.
+Identification of Human Limb Visco-Elasticity Using Robotics Methods to Support the Diagnosis of Neuromuscular
Diseases, Int. J. of Robotic Research (to be publiched)
神経病の診断
実験結果
Possible discrimination of patients
21
終わり
 参考論文


W. Khalil and E. Dombre. Modeling, identification and control of robots. Hermès Penton, London-U.K, 2002.
Y. Nakamura, Advanced Robotics: Redundancy and Optimization, Addison-Wesley Longman Publishing Co., Inc,
1990.

G. Venture, K. Ayusawa, and Y. Nakamura, “Motion capture based identification of human inertial parameters,” in
Proc. IEEE/EMBS Int. Conf. on Eng. in Medicine and Biology, 2008, pp. 4575–4578.
G. Venture, K. Ayusawa, and Y. Nakamura, “Dynamics identification of humanoid systems,” in Proc. CISM-IFToMM
Symp. on Robot Design, Dynamics, and Control (ROMANSY), 2008, pp. 301–308.
Y. Fujimoto, S. Obata, and A. Kawamura, “Robust biped walking with active interaction control between foot and
ground,” in Proc. of the IEEE Int. Conf. on Robotics and Automation, 1998, p. 2030˝U2035.
K. Yoshida, D.N. Nenchev, and M. Uchiyama, “Moving base robotics and reaction management control,” in Proc. of
the Seventh Int. Symp. of Robotics Research, 1995, p. 100˝U109.
H. Mayeda, K. Osuka, and A. Kangawa, “A new identification method for serial manipulator arms,” in Proc. IFAC 9th
World Congress, 1984, pp. 2429–2434.
C.G. Atkeson, C.H. An, and J.M. Hollerbach, “Estimation of inertial parameters of manipulator loads and lunks,” Int. J.
of Robotic Research, vol. 5, no. 3, pp. 101–119, 1986.
H. Kawasaki, Y. Beniya, and K. Kanzaki, “Minimum dynamics parameters of tree structure robot models,” in Int. Conf. of
Industrial Electronics, Control and Instrumentation, 1991, vol. 2, pp. 1100–1105.
W. Khalil and F. Bennis, “Symbolic calculation of the base inertial parameters of closed-loop robots,” Int. J. of Robotics
Research, vol. 14(2), pp. 112–128, April 1995.
M. Gautier, “Numerical calculation of the base inertial parameters,” J. of Robotic Systems, vol. 8(4), pp. 485–506, 1991.
K. Ayusawa, G. Venture, and Y. Nakamura, “Inertial parameters identifiability of humanoid robot based on the
baselink equation of motion,” Proc. of the Conf. on Robotics and Mechatronics, 2P1-F09, 2008, (in Japanese).
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