手术机器人需要有较高的稳定性和运动精度，且兼顾术前路径规划与术中反馈控制能力，这有助于提高微创外科手术机器人的灵巧性和可操作性并发掘其全部潜力。同时手术机器人术中自主避障及靶点精准操作需依靠实时的形状反馈信息。然而现阶段形状感知系统的速度、精度及其与手术器械的集成能力较差，难以满足复杂临床应用需求。近年来光纤传感器以其体积小易于集成、抗电磁干扰、应变敏感性、生物兼容性及分布式测量等优势，在手术器械形状感知领域应用越来越广泛。然而术中手术器械形状传感器非线性特性极大的限制了其感知精度。因此联合光纤光栅传感技术与人工智能技术进一步提高手术器械形状感知精度，并为手术机器人的自主/主从操作提供器械动态形状信息，最终实现空间姿态形状感知仍具有挑战性与重要意义。鉴于此本文将结合分布式光纤光栅传感网络与机器学习，研制微创医疗器械三维形状光纤光栅传感器，并构建形状感知与重构模型，本文主要展开的工作如下：

（1）环境噪声干扰会降低形状感知中分布式光纤光栅波长解调精度，进而降低手术器械形状重构准确性。针对这一问题，本文提出了一种融合小波软阈值去噪和Trip-Hop寻峰算法的分布式光纤光栅光谱信噪分离及波长寻峰算法，以实现中心波长的准确解调，为后续光纤光栅形状感知系统提供准确的输入数据。

（2）针对术中穿刺针形状实时反馈需求提出了一种波分混合复用的光纤光栅形状自感知穿刺针构型。然而光纤光栅形状自感知穿刺针在封装、制造及术中会产生各种非线性特征，使以微分几何理论为基础的形状重构算法速度与精度较差。针对上述问题本文提出了一种基于BP神经网络的形状重构方法以实现术中穿刺针空间形状实时感知测量，并开展了形状重构精度验证实验。

（3）针对运动空间及姿态维度更高的介入式导管形状感知问题，提出了一种光纤光栅形状感知传感器构型及其封装方法。考虑传感器封装过程的扭曲变形导致传感器难以精准标定等问题，提出基于BP神经网络算法和Frenet-Serret方程的形状重构理论。并结合HPO（Hunter-Prey Optimization, HPO）算法确定了形状感知神经网络模型最优参数。通过传感器多曲率标定实验，构建神经网络训练数据集，验证该模型在解决介入式导管形状传感器非线性问题上的可行性。

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