通过实时有限元模拟和交叉熵优化实现柔性针的高精度自主控制。
High-Precision Autonomous Control of Flexible Needles via Real-Time Finite Element Simulation and Cross-Entropy Optimization.
作者信息
Wang Yanzhou, Chang Chang, Mei Junling, Leonard Simon, Taylor Russell, Iordachita Iulian
机构信息
Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
Department of Computer Science and the Laboratory of Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
出版信息
IEEE Robot Autom Lett. 2025 Oct;10(10):10578-10585. doi: 10.1109/lra.2025.3604744. Epub 2025 Sep 2.
This paper presents a unified framework for autonomous flexible needle control in soft tissues using real-time finite element (FE) simulation and cross-entropy (CE) optimization. The method combines a sampling-based model predictive controller (MPC) for trajectory tracking with a kinematic-based bang-bang strategy to coordinate needle insertion, lateral adjustments, and bevel rotations. Sparse electromagnetic (EM) tracking feedback enables needle state reconstruction and compensates for model uncertainties. Experiments in plastisol and chicken breast phantoms show sub-millimeter targeting accuracy, with respective targeting errors 0.16 ± 0.29 mm and 0.22 ± 0.78 mm as reported by the tracker.
本文提出了一个统一框架，用于在软组织中进行自主柔性针控制，该框架采用实时有限元（FE）模拟和交叉熵（CE）优化。该方法将基于采样的模型预测控制器（MPC）用于轨迹跟踪，与基于运动学的继电控制策略相结合，以协调针的插入、横向调整和斜面旋转。稀疏电磁（EM）跟踪反馈能够实现针状态重建，并补偿模型不确定性。在增塑溶胶和鸡胸模型中的实验显示出亚毫米级的靶向精度，跟踪器报告的各自靶向误差为0.16±0.29毫米和0.22±0.78毫米。
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