论文标题
使用几何应变参数化对软连续臂的基于视觉的形状重建
Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization
论文作者
论文摘要
对软连续臂的兴趣增加了，因为它们的固有物质弹性使其与环境实现了安全和适应性的相互作用。但是，为了在这些臂上完全自主性，需要准确的三维形状传感。已经发现基于视觉的溶液可有效估计软连续臂的形状。在本文中，提出了一个基于视觉的形状估计器，该估计器利用基于几何应变的表示软臂的形状表示。该表示形式将弯曲形状的尺寸降低为有限的应变基函数集，从而可以对最适合观察到的图像的形状有效优化。实验结果证明了所提出的方法在估算终端效应子的效果下，准确性小于软臂的半径。还分析了多个基础函数并比较使用的特定软连续臂。
Interest in soft continuum arms has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However to achieve full autonomy in these arms, accurate three-dimensional shape sensing is needed. Vision-based solutions have been found to be effective in estimating the shape of soft continuum arms. In this paper, a vision-based shape estimator that utilizes a geometric strain based representation for the soft continuum arm's shape, is proposed. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Multiple basis functions are also analyzed and compared for the specific soft continuum arm in use.
