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.
翻译:本文涉及从多个相机测量中估算(内插和平滑)细体弹性体的形状(方形和六种变形模式)的问题,这个问题在生物学(薄体、软体和弹性体结构在各种物种之间普遍存在接触)和工程(特别是在软机器人领域)两方面都很重要。拟议的形状估计数学配方基于物理学信息,其方程式在弯曲、剪剪裁、扭动和伸展时将细体机理学编码为等式的Corserat特别棒理论的使用。这种方法用来得出在纤维强化和电缆驱动软机器人武器方面实验性地展示的数字算法。这些实验演示表明,这种方法准确(小于5毫米误差,比手臂直径低三倍),而且对噪音和不确定性具有很强性能。