论文标题
关于在中心状态估计中使用扭矩测量
On the Use of Torque Measurement in Centroidal State Estimation
论文作者
论文摘要
最先进的腿机器人可以在其驱动系统的输出处测量扭矩,或者具有透明的驱动系统,从而可以从电动电流计算关节扭矩。无论哪种情况,这种传感器模式很少用于状态估计。在本文中,我们建议使用关节扭矩测量值来估计腿部机器人的质心状态。为此,我们将腿部机器人的全身动力学投射到接触约束的无空间中,从而使动力学的表达与接触力无关。使用受约束的动力学和质心动量矩阵,我们能够直接将关节扭矩和质心状态动力学联系起来。使用结果模型作为扩展卡尔曼滤波器(EKF)的过程模型,我们将扭矩测量融合在质心估计问题中。通过具有不同步态的四倍机器人的实验实验,我们证明,与直接计算相比,基于扭矩的EKF的估计质心状态大大改善了这些量的回收率。
State of the art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot with different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation.