Sanchez Jose, Mohy El Dine Kamal, Corrales Juan Antonio, Bouzgarrou Belhassen-Chedli, Mezouar Youcef
Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, Clermont-Ferrand, France.
Front Robot AI. 2020 Jun 9;7:73. doi: 10.3389/frobt.2020.00073. eCollection 2020.
In this paper, we present a novel pipeline to simultaneously estimate and manipulate the deformation of an object using only force sensing and an FEM model. The pipeline is composed of a sensor model, a deformation model and a pose controller. The sensor model computes the contact forces that are used as input to the deformation model which updates the volumetric mesh of a manipulated object. The controller then deforms the object such that a given pose on the mesh reaches a desired pose. The proposed approach is thoroughly evaluated in real experiments using a robot manipulator and a force-torque sensor to show its accuracy in estimating and manipulating deformations without the use of vision sensors.
在本文中,我们提出了一种新颖的流程,仅使用力传感和有限元模型来同时估计和操纵物体的变形。该流程由传感器模型、变形模型和位姿控制器组成。传感器模型计算用作变形模型输入的接触力,变形模型更新被操纵物体的体网格。然后,控制器使物体变形,以使网格上的给定位姿达到期望位姿。使用机器人操纵器和力扭矩传感器在实际实验中对所提出的方法进行了全面评估,以展示其在不使用视觉传感器的情况下估计和操纵变形的准确性。