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Joint Torque Estimation using Base Force-Torque Sensor to Facilitate Physical Human-Robot Interaction (pHRI)

机译:使用基力扭矩传感器的联合扭矩估计,以促进物理人员机器人相互作用(PHRI)

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To detect forces during physical Human-Robot Interaction (pHRI), a force-torque sensor (FTS) is generally attached at the wrist of a robot manipulator. Alternatively, collaborative robots can measure interaction forces via torque sensing at their joints. Yet another direction toward safe and interactive robots is to cover them in smart skins with embedded tactile sensors. In this paper, we explore another idea to facilitate pHRI using an FTS placed at the base of a robot arm. The resulting base force-torque sensor (BFTS) is able to sense external forces and torques applied anywhere along the robot body. We formulate a model-free, on-line learning controller to estimate the interaction forces on the robot from the BFTS data. The controller does not require a robot dynamic model to operate, and has Lyapunov stability guarantees. We conduct experiments to validate the mean-square estimation error of our scheme using a custom 6-DOF robotic arm under real-time control. Results show that the measured torques at individual joints closely follow the estimated values. In the future, this controller can be used for adaptive pHRI with non-collaborative robots or robot manipulators.
机译:为了在物理人员机器人相互作用(PHRI)期间检测力,通常在机器人操纵器的手腕上附接力扭矩传感器(FT)。或者,协作机器人可以通过在其关节处通过扭矩感测来测量相互作用力。距离安全和交互式机器人的另一个方向是将它们覆盖在带有嵌入式触觉传感器的智能皮肤中。在本文中,我们探讨了另一种想法,以便使用放置在机器人臂底部的FTS。所得到的基力 - 扭矩传感器(BFT)能够感测沿机器人主体的任何地方施加的外力和扭矩。我们制定无模型在线学习控制器,以估计来自BFTS数据的机器人上的交互力。控制器不需要机器人动态模型来运行,并且具有Lyapunov稳定性保证。我们在实时控制下使用定制的6-DOF机器人臂验证我们方案的平均方形估计误差的实验。结果表明,各个关节的测量扭矩紧密遵循估计值。将来,该控制器可用于具有非协同机器人或机器人操纵器的自适应PHRI。

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