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.

翻译：磁驱动技术使手术机器人能够在复杂解剖路径中导航，同时减少组织创伤并提高手术精度。然而，临床部署受到荧光透视成像条件下控制系统挑战的限制，因为该成像方式提供低帧率且含噪声的位姿反馈。本文提出一种控制框架，通过结合直接输出线圈电流的非线性模型预测控制框架、基于泽尼克多项式的解析可微磁场模型以及用于估计机器人状态的卡尔曼滤波器，在此类条件下保持准确性与稳定性。实验验证采用两个磁机器人在3D打印流体工作空间及模拟硬膜外给药过程的脊柱体模中进行。结果表明，当反馈降采样至3 Hz并添加高斯噪声时，所提出的控制方法仍保持高精度。在脊柱体模实验中，该方法成功执行了给药轨迹，其均方根位置误差为1.18毫米，同时保持与关键解剖边界的安全距离。