Humanoid and Human Inertia Parameter Identification Using Hierarchical Optimization

Abstract

International audienceWe 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

Contact detection and physical interaction for low cost personal robots

“…Finally, we will investigate the residual method in the presence of link 's soft-covers instead of the current rigid ones. Finally, we could also use the residual to improve the robot inertia parameters identification [22].…”

Linear Matrix Inequalities for Physically Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution

“…Recent work has shown the benefits of including prior knowledge on the shape of each rigid body within parameter identification. Work by Jovic et al [12] for instance, enforced the CoM to reside within a bounding box estimated from CAD. New constraints are provided here that address second moments.…”

“…Thus, recent trends in control have been accompanied by many parallel developments in the area of system identification [10], [11], [12], [13], [14]. This recent work follows a rich history of research into inertial parameter identification, with seminal work in [15].…”

Whole body segment inertia parameters estimation from movement and ground reaction forces: a feasibility study

“…Nevertheless, these base parameters could be used to personalize the BSIP estimated from AT. Although promising, this idea has only been evaluated in two recent studies (Jovic et al 2016 andBonnet et al 2016). However, these studies either presented results for masses only (Jovic et al 2016), or used a complex procedure to guide long lasting (7 min) exciting motions (Bonnet et al 2016).…”

“…Although promising, this idea has only been evaluated in two recent studies (Jovic et al 2016 andBonnet et al 2016). However, these studies either presented results for masses only (Jovic et al 2016), or used a complex procedure to guide long lasting (7 min) exciting motions (Bonnet et al 2016). This study thus aims at evaluating the possibility to use identification techniques to evaluate segmental BSIP in a classical human movement analysis framework.…”