基于视觉的、采用几何应变参数化方法的软体连续臂形状重建
Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization
AI总结：
针对软体连续臂自主运行所需的精准形状感知需求，提出采用几何应变表示的视觉形状估计器，通过基函数降维实现高效优化，实验验证其末端估计误差小于臂半径，并对比了多种基函数效果。
摘要：
软体连续臂因其固有的材料弹性能够实现与环境的安全、自适应交互，相关研究兴趣日益增长。但要实现这类机械臂的完全自主，需要精准的三维形状感知能力。已有研究表明，基于视觉的方案可有效估计软体连续臂的形状。本文提出一种基于视觉的形状估计器，它采用基于几何应变的表示方法描述软体连续臂的形状。该表示方法将弯曲形状的维度降低为一组有限的应变基函数，从而能够高效优化出与观测图像最匹配的形状。实验结果证明了所提方法的有效性，其末端执行器的估计误差小于软体臂的半径。本文还针对所使用的特定软体连续臂，对多种基函数进行了分析与比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
