Real-time Friction Estimation for Grip Force Control

Heba Khamis, Benjamin Xia, S. Redmond
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引用次数: 9

Abstract

An important capability of humans when performing dexterous precision gripping tasks is our ability to feel both the weight and slipperiness of an object in real-time, and adjust our grip force accordingly. In this paper, we present for the first time a fully-instrumented version of our PapillArray tactile sensor concept, which can sense grip force, object weight, and incipient slip and friction, all in real-time. We demonstrate the real-time estimation of friction and measurement of 3D force from PapillArray sensors mounted on each finger of a two-finger gripper, combined with a closed-loop grip-force control algorithm that dynamically applies a near-optimal grip force to avoid dropping objects of varying weight and friction. A vertical lifting task was performed using an object with varying weight and friction, and with some common household items. After intentionally adding a 20% safety margin on the target grip force, the actual grip force applied was only 9-30 % greater than that required to avoid slip. Future work will focus on incorporating real-time torque measurement into the grip force feedback control. This will significantly advance the state-of-the-art in artificial tactile sensing and bring us closer to robotic dexterity.
握把力控制的实时摩擦估计
人类在执行灵巧的精确抓握任务时的一个重要能力是我们能够实时感知物体的重量和滑度,并相应地调整我们的抓握力。在本文中,我们首次展示了我们的PapillArray触觉传感器概念的全仪器版本,它可以实时感知握力,物体重量以及初始滑动和摩擦。我们演示了安装在两指夹持器的每个手指上的PapillArray传感器对摩擦力的实时估计和3D力的测量,并结合闭环夹持力控制算法,该算法动态应用接近最佳的夹持力,以避免不同重量和摩擦力的物体脱落。使用不同重量和摩擦力的物体以及一些常见的家用物品进行垂直提升任务。在有意在目标握持力上增加20%的安全裕度后,实际施加的握持力仅比避免打滑所需的握持力大9- 30%。未来的工作将集中于将实时扭矩测量纳入抓地力反馈控制。这将极大地推动人工触觉传感技术的发展,使我们更接近机器人的灵巧性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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