用数学模型和智能算法，让磁控手术机器人在低清影像下仍能精准导航。
Fluoroscopy-Constrained Magnetic Robot Control via Zernike-Based Field Modeling and Nonlinear MPC
- 用泽尼克多项式建模磁场，结合非线性预测控制直接输出线圈电流。
- 在3Hz低帧率+2mm噪声下仍保持1.18mm的定位误差。
- 适合需要微创、高精度的脊柱药物输送等临床场景。
磁驱动可使手术机器人在复杂解剖路径中导航，减少组织损伤并提升精度。但临床应用受限于荧光透视成像带来的低帧率和噪声姿态反馈。本文提出一种控制框架，通过基于泽尼克多项式的解析可微磁场模型、直接输出线圈电流的非线性模型预测控制（NMPC），以及卡尔曼滤波估计机器人状态，在3D打印流体工作区与脊柱假体中验证。实验显示，当反馈降为3Hz并添加标准差2mm的高斯噪声时，该方法仍保持高精度；在脊柱假体中执行药物输送轨迹，均方根（RMS）位置误差达1.18mm，且始终远离关键解剖边界。
原文摘要 · Abstract (English)
Magnetic actuation enables surgical robots to navigate complex anatomical pathways while reducing tissue trauma and improving surgical precision. However, clinical deployment is limited by the challenges of controlling such systems under fluoroscopic imaging, which provides low frame rate and noisy pose feedback. This paper presents a control framework that remains accurate and stable under such conditions by combining a nonlinear model predictive control (NMPC) framework that directly outputs coil currents, an analytically differentiable magnetic field model based on Zernike polynomials, and a Kalman filter to estimate the robot state. Experimental validation is conducted with two magnetic robots in a 3D-printed fluid workspace and a spine phantom replicating drug delivery in the epidural space. Results show the proposed control method remains highly accurate when feedback is downsampled to 3 Hz with added Gaussian noise (sigma = 2 mm), mimicking clinical fluoroscopy. In the spine phantom experiments, the proposed method successfully executed a drug delivery trajectory with a root mean square (RMS) position error of 1.18 mm while maintaining safe clearance from critical anatomical boundaries.
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