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首页> 外文期刊>Journal of Mechanical Science and Technology >Sensor-less external force detection for industrial manipulators to facilitate physical human-robot interaction
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Sensor-less external force detection for industrial manipulators to facilitate physical human-robot interaction

机译:用于工业机械手的传感器外力检测,以促进物理人员机器人互动

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Sensor-less external force detection is important for industrial robots which are usually not equipped with external force sensors to be applied in physical human-robot interaction (pHRI). This paper adopts the dynamic models of the robot in both dynamic mode and quasistatic mode to detect the external force. In the dynamic mode, the inertia and friction parameters of the robot are identified with the weighted least squares. The excitation trajectory for parameter identification is optimised. The un-modelled peak points in the joint torque residual are removed by a statistical method. The torque changes of joints in quasi-static mode which are equivalent to the joint pre-sliding friction is modelled with a lumped parameter model, generalised Maxwell slip (GMS) element model. Therefore, there is no need for the switching between the friction models in different modes and this and facilitates the application of dynamic model in the external force detection. The dynamic models of robots both in dynamic mode and quasi-static mode and their effectiveness for external force detection in pHRI are verified by experimental results.
机译:更少的外力检测对于工业机器人来说重要的是通常不配备的外力传感器以应用于物理人员机器人相互作用(PHRI)。本文采用动态模式和Quasistatic模式中机器人的动态模型来检测外力。在动态模式中,机器人的惯性和摩擦参数用加权最小二乘识别。优化参数识别的激励轨迹。通过统计方法除去联合扭矩残余的未建模的峰值。等同于接头预滑摩擦的准接头的扭矩变化与集成的参数模型,广义麦克斯韦(GMS)元素模型建模。因此,不需要在不同模式下的摩擦模型之间切换,并促进动态模型在外力检测中的应用。通过实验结果验证了动态模式和准静态模式下的机器人动态模型及其对PHRI中的外力检测的有效性。

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