Sefati Shahriar, Gao Cong, Iordachita Iulian, Taylor Russell H, Armand Mehran
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA, 21218.
Department of Orthopedic Surgery, The Johns Hopkins Medical School, Baltimore, MD, USA, 21205.
IEEE Sens J. 2021 Feb 1;21(3):3066-3076. doi: 10.1109/jsen.2020.3028208. Epub 2021 Oct 1.
This article proposes a data-driven learning-based approach for shape sensing and Distal-end Position Estimation (DPE) of a surgical Continuum Manipulator (CM) in constrained environments using Fiber Bragg Grating (FBG) sensors. The proposed approach uses only the sensory data from an unmodeled uncalibrated sensor embedded in the CM to estimate the shape and DPE. It serves as an alternate to the conventional mechanics-based sensor-model-dependent approach which relies on several sensor and CM geometrical assumptions. Unlike the conventional approach where the shape is reconstructed from proximal to distal end of the device, we propose a reversed approach where the distal-end position is estimated first and given this information, shape is then reconstructed from distal to proximal end. The proposed methodology yields more accurate DPE by avoiding accumulation of integration errors in conventional approaches. We study three data-driven models, namely a linear regression model, a Deep Neural Network (DNN), and a Temporal Neural Network (TNN) and compare DPE and shape reconstruction results. Additionally, we test both approaches (data-driven and model-dependent) against internal and external disturbances to the CM and its environment such as incorporation of flexible medical instruments into the CM and contacts with obstacles in taskspace. Using the data-driven (DNN) and model-dependent approaches, the following max absolute errors are observed for DPE: 0.78 mm and 2.45 mm in free bending motion, 0.11 mm and 3.20 mm with flexible instruments, and 1.22 mm and 3.19 mm with taskspace obstacles, indicating superior performance of the proposed data-driven approach compared to the conventional approaches.
本文提出了一种基于数据驱动学习的方法，用于在受限环境中使用光纤布拉格光栅（FBG）传感器对手术连续体操纵器（CM）进行形状感知和远端位置估计（DPE）。所提出的方法仅使用嵌入在CM中的未建模、未校准传感器的传感数据来估计形状和DPE。它是传统的基于力学的传感器模型依赖方法的替代方法，传统方法依赖于几个传感器和CM几何假设。与从设备近端到远端重建形状的传统方法不同，我们提出了一种反向方法，即首先估计远端位置，并根据此信息从远端到近端重建形状。所提出的方法通过避免传统方法中积分误差的积累，产生更准确的DPE。我们研究了三种数据驱动模型，即线性回归模型、深度神经网络（DNN）和时间神经网络（TNN），并比较了DPE和形状重建结果。此外，我们针对CM及其环境的内部和外部干扰测试了两种方法（数据驱动和模型依赖），例如将柔性医疗器械纳入CM以及在任务空间中与障碍物接触。使用数据驱动（DNN）和模型依赖方法，在DPE方面观察到以下最大绝对误差：自由弯曲运动中为0.78毫米和2.45毫米，使用柔性器械时为0.11毫米和3.20毫米，遇到任务空间障碍物时为1.22毫米和3.19毫米，这表明所提出的数据驱动方法比传统方法具有更好的性能。
