用一根光纤实现柔性机械臂接触检测与力估计
A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber
- 分步学习框架：先用梯度提升检测接触，再用CNN-FiLM模型定位和估力
- 单根分布式光纤即可在复杂环境中实现接触三要素联合推断
- 适合做微创手术机器人感知系统的研究者参考
连续体机械臂（CMs）因其柔顺结构和深入狭窄解剖环境的能力，广泛应用于微创手术。然而，其分布式形变使得接触检测、位置定位及力估计极具挑战，尤其当交互发生在未知弧长位置时。为此，本文提出一种级联学习框架（CLF），针对集成单根分布式光学频率域反射计（OFDR）光纤的机械臂。该光纤沿机械臂一侧分布，提供密集应变测量，捕捉外部交互引起的应变扰动。所提CLF首先通过梯度提升分类器检测接触，随后利用CNN-FiLM模型预测机械臂上空间力分布，以估计接触位置与作用力大小。在受阻环境下对腱驱动式传感器化机械臂的实验验证表明，单根分布式OFDR光纤即可提供足够信息，实现对接触发生、位置及力的联合推断。
原文摘要 · Abstract (English)
Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented 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, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.
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