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Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

机译:KUKA LWR型机器人的力标定,包括嵌入式关节扭矩传感器和机器人结构

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The Kuka LWR is equipped with torque sensors mounted into the actuated joints. Each torque sensor is calibrated separately before it is mounted on the robot. This needs a second calibration at the last stage of the assembling of the robot in order to take into account the effect of the robot structure through it's jacobian matrix. This final calibration is necessary to improve the accuracy of the estimation of the interaction wrench of the robot with its environment. However, the proposed calibration techniques are usually complicated, time-consuming, and must be carried out before assembling the sensors on the robot. In this paper, a simple and fast method for calibrating the sensors once they are assembled on the robot is presented. The method is based on the least squares solution of an over-determined linear system obtained with the robot inverse dynamic identification model in which are included the sensor gains. This model is calculated with available sensor measurement and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on the Kuka LWR4+ but can be applied to any similar kind of robot equipped with joint torque sensors.
机译:Kuka LWR配备有安装到致动关节中的扭矩传感器。在安装在机器人上之前,每个扭矩传感器都是单独校准的。这需要在机器人组装的最后阶段进行第二次校准,以便考虑机器人结构通过它是雅各的矩阵的影响。该最终校准是为了提高机器人交互扳手的准确性,以其环境。然而,所提出的校准技术通常复杂,耗时,并且必须在组装机器人上的传感器之前进行。在本文中,提出了一种简单而快速的方法,可以在机器人上组装在机器人上进行校准传感器。该方法基于利用机器人逆动态识别模型获得的过分确定的线性系统的最小二乘解,其中包括传感器增益。该模型用可用的传感器测量和接头位置采样数据计算,而机器人正在跟踪一些参考轨迹,而没有机器人的负载以及一些具有已知有效载荷的轨迹固定在机器人上。该方法在Kuka LWR4 +上进行实验验证,但可以应用于配备有关节扭矩传感器的任何类似的机器人。

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