📄 中文摘要
连续操纵器（CMs）因其柔性结构和在狭窄解剖环境中导航的能力，广泛应用于微创手术。然而，其分布式变形使得力传感、接触检测、定位和力估计面临挑战，尤其是在机器人沿未知弧长位置发生交互时。为了解决这一问题，提出了一种级联学习框架（CLF），该框架针对沿机器人一侧嵌入单根分布式光频域反射测量（OFDR）光纤的CMs。OFDR传感器提供了沿操纵器主干的密集应变测量，捕捉外部交互引起的应变扰动。
基于学习的方法用于连续操纵器的接触检测、定位和力估计，集成光频域反射测量光纤
📄 English Summary
A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber
Continuum manipulators (CMs) are extensively utilized in minimally invasive procedures due to their compliant structure and capability to navigate deep and confined anatomical environments. However, their distributed deformation poses significant challenges for force sensing, contact detection, localization, and force estimation, particularly when interactions occur at unknown arc-length locations along the robot. To tackle this issue, a cascade learning-based framework (CLF) is proposed for CMs equipped with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, effectively capturing strain perturbations induced by external interactions.