A numerical method for choosing motions with optimal excitation properties for identification of biped dynamics - An application to human
A numerical method for choosing motions with optimal excitation properties for identification of biped dynamics - An application to human
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DOI：
10.1109/robot.2009.5152264
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
G. Venture;K. Ayusawa;Yoshihiko Nakamura
中科院分区：
文献类型：
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作者：
G. Venture;K. Ayusawa;Yoshihiko Nakamura
识别结果很大程度上取决于用于采样识别模型的运动的激励特性。针对具有少量自由度的机械臂机器人，提出了一种定义持久激励轨迹的策略。然而，由于自由度的重要性，它们不容易推广到类人系统和人体；经验知识通常用于产生和选择持续的兴奋运动。本文提出了一种从已有数据集中选择持久激励运动的方法，以优化识别结果和计算时间。该方法是基于利用由基杆方程得到的足类系统辨识模型。该方法不考虑回归量矩阵的条件数，而是将回归量分解为初等子回归量，并计算每个子回归量的条件数。然后提出一个选择规则。利用40个运动数据集对整体方法进行了人体惯性参数识别实验。给出了不同运动组合的比较结果。
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 sub-regressors 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.