搏动组织中神经线程放置的插入工具的基于预览的相对运动控制

中文摘要 AI 辅助

机器人神经线程放置需要调节插入工具尖端，使其相对于随心脏和呼吸搏动的组织保持相对位置。本文开发了一种基于预览的相对运动控制器，该控制器估计由延迟引起的周期性表面运动，在短时间范围内预测该运动，并使用无偏移模型预测控制来调节相对放置，同时限制执行器的作用力和横向相对速度。在MuJoCo仿真中，1自由度控制器在自由空间的相对放置RMS误差为12.0μm，接触时为1.9μm；相比之下，延迟反馈阻抗控制器的对应误差为18.3/176.8μm，实验室坐标系PD控制器为286.1/275.5μm。其代价是接触峰值力更高（3.43 mN，而对照组为2.00 mN），因为无偏移跟踪会使尖端完全达到指令深度，而非顺应组织。在3自由度场景中，耦合预览将接触横向剪切从1.34 mm/s降至0.50 mm/s，横向RMS误差为2.1μm。一种可行性恢复的八边形剪切公式通过添加有界共享松弛变量，使QP在传感性能下降时仍可求解：在每轴10μm RMS传感噪声下，仅匹配成本的控制器在全部10次试验中均违反0.80 mm/s的预算（均值/最大值为0.988/1.175 mm/s），而软八边形控制器完成全部10次试验，无弃权（不执行）且无实测违反（0.653/0.712 mm/s），其工作范围可达15μm RMS。控制器实际有限时间范围误差反馈增益的两顶点李雅普诺夫证书，在-40%/+50%的反射质量失配下成立。本研究仅为仿真，建模的尖端为刚性接触点，部署前仍需考虑柔性线程和载针力学、经验证的瞬态力约束、生物损伤阈值，以及硬件级的传感和时序特性。

英文摘要

Flexible neural electrode threads must be placed at a prescribed depth while the cortical surface moves with cardiac and respiratory pulsation. A controller tracking a fixed point in the laboratory frame cannot distinguish commanded insertion from tissue motion; the error appears as both a depth offset and relative tip--tissue velocity during contact. This paper formulates thread insertion in tissue-relative coordinates: a harmonic observer predicts delayed cortical-surface motion over the control horizon, a constrained MPC regulates the tip relative to that prediction while limiting actuator effort and lateral relative velocity, and an augmented disturbance state removes the steady offset from persistent contact force and model mismatch. In a 1-DOF MuJoCo benchmark, the controller reaches RMS relative-placement errors of 12.0\um\ free-space and 1.9\um\ in contact, versus 18.3/176.8\um\ for delayed-feedback impedance and 286.1/275.5\um\ for laboratory-frame PD -- the lower contact offset costs more peak contact force (3.43 vs.\ 2.00~mN), since it drives to commanded depth rather than yielding to tissue. A 3-DOF extension reduces lateral shear velocity from 1.34 to 0.50~mm/s at 2.1\um\ lateral placement error, and a feasibility-restoring soft-slack formulation keeps the shear constraint solvable under degraded sensing where a matched hard-constraint controller fails. A two-vertex Lyapunov certificate for the finite-horizon gain holds over $-40\%/{+}50\%$ reflected-mass mismatch, and the 1-DOF QP solves in under 0.4~ms at the 95th percentile. These results are a simulation-based control benchmark, not a clinical safety claim: the modeled tip is a rigid contact point, and flexible-thread mechanics, a validated force constraint, biological damage thresholds, and hardware-realistic sensing and timing remain necessary before deployment.

发表机构

• Voryx Robotic LLC（沃里克斯机器人有限责任公司）

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