Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270 degrees and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space and constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

翻译：近年来，光纤布拉格光栅等光纤传感器被广泛研究用于柔性手术机器人的形状重构与力估计。然而，现有方法大多需要精确掌握光纤内FBG的模型参数及其与柔性机器人的对准关系，才能获得准确的感知结果。另一个挑战在于实时获取柔性机器人任意位置处的外力，这在机器人手术中遇到大挠度变形时尤为必要。本文提出一种新颖的数据驱动范式，利用单芯FBG光纤的稀疏应变测量，实现高度可变形柔性机器人沿程形状与力的同步估计。我们设计了一种薄壁柔性传感管，将FBG传感器呈螺旋状嵌入其中，用于最大偏转角度达270度、弯曲半径小于10毫米的机器人辅助柔性输尿管镜。通过引入空间应变编码器，研究并比较了三种学习模型在自由空间与具有不同位置接触力的受限环境中的性能表现。动态形状-力感知精度的实验结果证明了所提方法的有效性与优越性。