Humanoid and Human Inertia Parameter Identification Using Hierarchical Optimization

Jovana Jovic 1 Adrien Escande Ko Ayusawa 2 Eiichi Yoshida 2 Abderrahmane Kheddar 3, 1 Gentiane Venture 4
1 CNRS-AIST JRL - Joint Robotics Laboratory [Japan]
2 CNRS-AIST JRL - Joint Robotics Laboratory [France]
3 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 Tokyo University of Agriculture and Technology
Abstract : We propose a method for estimation of humanoid and human links' inertial parameters. Our approach formulates the problem as a hierarchical quadratic program by exploiting the linear properties of rigid body dynamics with respect to the inertia parameters. In order to assess our algorithm, we conducted experiments with a humanoid robot and a human subject. We compared ground reaction forces and moments estimated from force measurements with those computed using identified inertia parameters and movement information. Our method is able to accurately reconstruct ground reaction forces and force moments. Moreover, our method is able to estimate correctly masses of the robots links and to accurately detect additional masses placed on the human subject during the experiments.
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Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01348410
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Submitted on : Saturday, July 23, 2016 - 7:38:29 AM
Last modification on : Friday, December 20, 2019 - 1:19:27 AM

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LIRMM | CNRS | TDS-MACS | IDH | MIPS | UNIV-MONTPELLIER | UNIV-RENNES | INSA-RENNES

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Jovana Jovic, Adrien Escande, Ko Ayusawa, Eiichi Yoshida, Abderrahmane Kheddar, et al.. Humanoid and Human Inertia Parameter Identification Using Hierarchical Optimization. IEEE Transactions on Robotics, IEEE, 2016, 32 (3), pp.726-735. ⟨10.1109/TRO.2016.2558190⟩. ⟨lirmm-01348410⟩

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