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首页> 外文期刊>Journal of Robotic Systems >Haptic Perception of Shape and Hollowness of Deformable Objects Using the Anthrobot-III Robot Hand
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Haptic Perception of Shape and Hollowness of Deformable Objects Using the Anthrobot-III Robot Hand

机译：使用Anthrobot-III机械手对可变形物体的形状和空心度的触觉感知

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This article presents a methodology for the haptic perception of contour shapes of almost planar objects grasped by a five-fingered robot hand as well as the detection of any object cavity. The originality of our approach resides in (1) finding the reaction force patterns at the fingertips of a five-fingered robot hand that grasps different deformable objects (forward problem) and (2) using these contact force patterns to find the shapes of grasped objects (inverse problem) and (3) to determine material defects such as holes in an object with identified shape. Contact force patterns are generated in the forward problem by the finite element method (DEM) and the shape identification in the inverse problem is realized by a supervised neural network architecture using the backpropagation algorithm. Forrlowing shape identification, detection of holes is performed by clustering actual and prototypical contact force patterns using the self-organizing feature maps of neural gas networks as an unsupervised hole-screening method.
机译：本文提出了一种方法，可以用触觉感知五指机械手抓住的几乎是平面物体的轮廓形状，以及检测任何物体腔。我们方法的独创性在于（1）在五只手指的指尖上找到反作用力模式，该五指可抓握不同的可变形物体（正向问题），并且（2）使用这些接触力模式来找到所握持物体的形状（反问题）和（3）确定材料缺陷，例如具有确定形状的物体中的孔。通过有限元方法（DEM）在正向问题中生成接触力模式，并通过使用反向传播算法的有监督神经网络体系结构在逆向问题中实现形状识别。放弃形状识别，使用神经气体网络的自组织特征图作为无监督的孔筛选方法，通过对实际和原型接触力模式进行聚类来执行孔的检测。

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