Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
复制标题
基于视觉和触觉测量的概率表面摩擦估计
DOI：
10.1109/lra.2021.3062585
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发表时间：
2020
影响因子：
5.2
通讯作者：
V. Kyrki
中科院分区：
文献类型：
--
作者：
Tran Nguyen Le;Francesco Verdoja;Fares J. Abu;V. Kyrki
准确地模拟物体的局部表面特性对许多机器人应用至关重要，从抓取到材料识别。然而，摩擦等表面特性很难估计，因为对物体的视觉观察并不能传达足够的这些特性信息。相比之下，触觉探索是耗时的，因为它只提供与对象的探索部分相关的信息。在这封信中，我们提出了一种联合视觉-触觉对象模型，该模型通过利用视觉和触觉信息的相关性以及机械臂的有限触觉探索来估计整个对象的表面摩擦系数。我们通过展示其在一系列真实多材料物体上估计不同摩擦系数的能力来证明所提出方法的有效性。此外，我们说明了估计的摩擦系数如何通过引导抓取规划器朝向高摩擦区域来提高抓取成功率。
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this letter, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas.
移动机器人上对象模型的自主学习
影响因子：
5.2
作者：
Faulhammer T
通讯作者：
Faulhammer T