Exciting trajectories for the identification of base inertial parameters of robots
International Journal of Robotics Research
Symbolic calculation of the base inertial parameters of closed-loop robots
International Journal of Robotics Research
Modeling, Identification and Control of Robots
Modeling, Identification and Control of Robots
Modeling and Identification of Passenger Car Dynamics Using Robotics Formalism
IEEE Transactions on Intelligent Transportation Systems
Natural motion animation through constraining and deconstraining at will
IEEE Transactions on Visualization and Computer Graphics
Optimal estimation of human body segments dynamics using realtime visual feedback
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Identification results dramatically depend on the excitation properties of the motion used to sample the identification model. Strategies to define persistent exciting trajectories have been developed for manipulator robots with few DOF. However they can not easily be extended to humanoid systems and humans due to the important number of DOF; and empirical knowledge is often used to generate and select persistent exciting motions. In this paper we propose a method to choose persistent exciting motions from an existing dataset in order to optimize both the identification results and the computation time. This method is based on the use of the identification model of legged systems obtained from the base-link equations. Instead of using well-established consideration on the condition number of the regressor matrix, the method uses a decomposition of the regressor into elementary subregressors and the computation of the condition number for each. A selection rule is then proposed. The overall method is experimentally tested to identify the human body inertial parameters using a data-set of 40 motions. Comparative results obtained from different combinations of motions are given.