一种融合物理的基于视觉的方法用于重建细长体的全部变形模式
A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies
AI总结：
本文提出了一种融合物理的基于视觉的方法，用于准确重建细长柔性体的全部变形模式，通过实验验证其在软机器人中的应用效果。
摘要：
本文关注从多个摄像头测量中估计（插值和平滑）细长柔性体的形状（姿态和六种变形模式）的问题。这个问题在生物学中尤为重要，因为在不同物种中普遍遇到细长、柔软和弹性结构；在工程领域，特别是在软机器人领域也具有重要意义。所提出的数学公式基于特殊柯西-拉格朗日杆理论，其方程编码了在弯曲、剪切、扭转和拉伸存在下的细长体力学。该方法用于推导数值算法，并通过纤维增强和缆驱动软机器人臂的实验进行验证。这些实验演示表明，该方法准确度高（<5毫米误差，仅为臂直径的三分之一）且对噪声和不确定性具有鲁棒性。
英文摘要：
This paper is concerned with the problem of estimating (interpolating and smoothing) the shape (pose and the six modes of deformation) of a slender flexible body from multiple camera measurements. This problem is important in both biology, where slender, soft, and elastic structures are ubiquitously encountered across species, and in engineering, particularly in the area of soft robotics. The proposed mathematical formulation for shape estimation is physics-informed, based on the use of the special Cosserat rod theory whose equations encode slender body mechanics in the presence of bending, shearing, twisting and stretching. The approach is used to derive numerical algorithms which are experimentally demonstrated for fiber reinforced and cable-driven soft robot arms. These experimental demonstrations show that the methodology is accurate (<5 mm error, three times less than the arm diameter) and robust to noise and uncertainties.
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