Abstract
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.
Abstract (translated)
近年来，光纤传感器（如光纤布拉格光栅）已广泛应用于柔性手术机器人的形状重建和力估计。然而，大多数现有方法需要光纤内FBGs的准确模型参数和其与柔性机器人的对齐，以实现准确的感测结果。另一个挑战是在柔性机器人上在线获取任意位置的外力，这在机器人手术中在大角度摆动时非常重要。在本文中，我们提出了一种通过单纤光纤的稀疏应变测量同时估计形状和力量的新型数据驱动方法。设计了一种由FBG传感器缠绕而成的薄壁软感测管，用于具有高达270度的弯曲和不到10毫米的弯曲半径的大弯曲柔性内窥镜。我们引入并研究了三种学习模型，通过集成空间应变编码器，在自由空间和约束环境中比较它们的性能。关于动态形状-力感测精度的实验结果表明，所提出的方法的有效性和优越性得到了充分证明。
URL
https://arxiv.org/abs/2404.16952
