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Efficient Break-Away Friction Ratio and Slip Prediction Based on Haptic Surface Exploration

机译:基于触觉表面探索的有效折断摩擦比和滑移预测

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摘要

The break-away friction ratio (BF-ratio), which is the ratio between friction force and the normal force at slip occurrence, is important for the prediction of incipient slip and the determination of optimal grasping forces. Conventionally, this ratio is assumed constant and approximated as the static friction coefficient. However, this ratio varies with acceleration rates and force rates applied to the grasped object and the object material, which lead to difficulties in determining optimal grasping forces that avoid slip. In this paper, we propose a novel approach based on the interactive forces to allow a robotic hand to predict object slip before its occurrence. The approach only requires the robotic hand to have a short haptic surface exploration over the object surface before manipulating it. Then, the frictional properties of the finger–object contact can be efficiently identified, and the BF-ratio can be real-time predicted to predict slip occurrence under dynamic grasping conditions. Using the predicted BF-ratio as a slip, threshold is demonstrated to be more accurate than using the static/Coulomb friction coefficient. The presented approach has been experimentally evaluated on different object surfaces, showing good performance in terms of prediction accuracy, robustness, and computational efficiency.
机译:脱离摩擦比(BF-ratio)是发生滑动时摩擦力与法向力之间的比率,对于预测初始滑动和确定最佳抓握力非常重要。按照惯例,该比率假定为常数,并且近似为静摩擦系数。然而,该比率随施加到被抓物体和物体材料上的加速度和力的比率而变化,这导致难以确定避免滑动的最佳抓力。在本文中,我们提出了一种基于交互作用力的新颖方法,可以使机器人手在物体滑移之前预测其滑移。该方法仅要求机械手在操作对象表面之前先对其进行短暂的触觉表面探索。然后,可以有效地识别手指与物体接触的摩擦特性,并且可以实时预测BF比,以预测在动态抓握条件下的滑动发生。使用预测的BF比作为滑动,已证明阈值比使用静/库仑摩擦系数更准确。所提出的方法已经在不同的物体表面上进行了实验评估,在预测精度,鲁棒性和计算效率方面显示出良好的性能。

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