Six axis force-torque sensors are commonly attached to the wrist of serial robots to measure the external forces and torques acting on the robot's end-effector. These measurements are used for load identification, contact detection, and human-robot interaction amongst other applications. Typically, the measurements obtained from the force-torque sensor are more accurate than estimates computed from joint torque readings, as the former is independent of the robot's dynamic and kinematic models. However, the force-torque sensor measurements are affected by a bias that drifts over time, caused by the compounding effects of temperature changes, mechanical stresses, and other factors. In this work, we present a pipeline that continuously estimates the bias and the drift of the bias of a force-torque sensor attached to the wrist of a robot. The first component of the pipeline is a Kalman filter that estimates the kinematic state (position, velocity, and acceleration) of the robot's joints. The second component is a kinematic model that maps the joint-space kinematics to the task-space kinematics of the force-torque sensor. Finally, the third component is a Kalman filter that estimates the bias and the drift of the bias of the force-torque sensor assuming that the inertial parameters of the gripper attached to the distal end of the force-torque sensor are known with certainty.

翻译：六轴力-扭矩传感器通常安装于串联机器人腕部，用于测量作用在机器人末端执行器上的外部力和力矩。这些测量结果广泛应用于负载识别、接触检测、人机交互等领域。通常情况下，力-扭矩传感器的测量精度优于基于关节力矩读数计算得到的估计值，因为前者独立于机器人的动力学和运动学模型。然而，力-扭矩传感器测量会受到随时间漂移的偏差影响，该偏差由温度变化、机械应力等因素的复合效应产生。本研究提出了一套流水线方法，可持续估计安装在机器人腕部的力-扭矩传感器的偏差及其漂移量。该流水线的首个组件是用于估计机器人关节运动学状态（位置、速度、加速度）的卡尔曼滤波器；第二个组件是将关节空间运动学映射至力-扭矩传感器任务空间运动学的运动学模型；最后，第三个组件是在假定力-扭矩传感器远端所夹持夹具的惯性参数完全已知的条件下，用于估计力-扭矩传感器偏差及其漂移量的卡尔曼滤波器。