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.

翻译：当前最先进的足式机器人要么能够在其驱动系统输出端测量扭矩，要么具有透明驱动系统，可通过电机电流计算关节扭矩。然而，这种传感器模态在状态估计中很少被使用。本文提出利用关节扭矩测量来估计足式机器人的质心状态。为此，我们将足式机器人的全身动力学投影至接触约束的零空间，从而推导出与接触力无关的动力学表达式。利用约束动力学与质心动量矩阵，我们能够直接关联关节扭矩与质心状态动力学。将所得模型作为扩展卡尔曼滤波器的过程模型后，我们将扭矩测量融入质心状态估计问题中。通过在四足机器人上采用不同步态进行的真实世界实验，我们证明：与直接计算方法相比，基于扭矩扩展卡尔曼滤波器估计的质心状态在恢复这些物理量方面有显著改善。