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Self-Generation of Optimal Exciting Motions for Identification of a Humanoid Robot

Vincent Bonnet 1 Kai Pfeiffer 2 Philippe Fraisse 3 André Crosnier 3 Gentiane Venture 4
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
3 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Knowledge of the inertial parameters of a humanoid robot is crucial for the development of model-based controllers or realistic simulation and motion planning in dynamic situations. Inertial parameters are usually provided from CAD data and thus are inaccurate especially if the robot is modified over time. Recent results showed that the inertial parameters specific to each robot can be identified using the external ground reaction forces and moments. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion and of the system’s specific level of measurement artefact/noise. In this paper, a new method for obtaining a system’s specific pseudo-online optimal set of optimal exciting motions (OEM) is proposed. This method is based on a real-time balance controller and on an optimization process to generate OEM while handling mechanical constraints. The method was experimentally validated with an NAO humanoid robot and a laboratory grade force-plate. The method shows a reduction of 1.6 times in average of the RMS difference between measured external ground reaction forces and moments and their estimates from CAD data.
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Submitted on : Monday, February 25, 2019 - 1:34:37 PM
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Vincent Bonnet, Kai Pfeiffer, Philippe Fraisse, André Crosnier, Gentiane Venture. Self-Generation of Optimal Exciting Motions for Identification of a Humanoid Robot. International Journal of Humanoid Robotics, World Scientific Publishing, 2018, 15 (6), pp.1850024. ⟨10.1142/S021984361850024X⟩. ⟨hal-02048085⟩



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