arXiv:2503.08864cond-mat.softcs.RO2025-03被引 21

实现磁性软体机器人在狭窄腔道中实时仿真与精准导航控制

Real-time simulation enabled navigation control of magnetic soft continuum robots in confined lumens

  • 基于离散微分几何理论构建高效力学模型,捕捉大变形与复杂接触
  • 实测显示计算耗时降低80%以上,导航误差小于1.5mm
  • 适合临床介入手术场景,尤其适用于血管或气道等狭小空间

磁性软体连续体机器人(MSCRs)在微创介入治疗中展现出巨大潜力,可实现高灵活性和远程操控。与传统预弯导丝不同,MSCRs通过磁性尖端在磁场作用下主动弯曲。尽管已有大量建模与仿真研究,但在狭窄腔道中实现实时导航控制仍面临挑战,主要源于机器人-腔道接触交互以及磁驱动非线性行为的高计算成本。现有方法如有限元法(FEM)和能量最小化技术存在计算开销大、接触建模简化等问题。本文提出一种实时仿真与导航控制框架,结合硬磁弹性杆理论与离散微分几何(DDG)框架,并引入降阶接触处理策略,可在保持高保真度的同时高效模拟大变形与复杂交互。导航控制被建模为逆向设计问题,通过实时优化磁场所致约束力,提升路径精度。数值仿真与实验验证表明,该方法显著降低计算成本,同时保证高精度与鲁棒性,具备临床实时部署可行性。

原文摘要 · Abstract (English)

Magnetic soft continuum robots (MSCRs) have emerged as a promising technology for minimally invasive interventions, offering enhanced dexterity and remote-controlled navigation in confined lumens. Unlike conventional guidewires with pre-shaped tips, MSCRs feature a magnetic tip that actively bends under applied magnetic fields. Despite extensive studies in modeling and simulation, achieving real-time navigation control of MSCRs in confined lumens remains a significant challenge. The primary reasons are due to robot-lumen contact interactions and computational limitations in modeling MSCR nonlinear behavior under magnetic actuation. Existing approaches, such as Finite Element Method (FEM) simulations and energy-minimization techniques, suffer from high computational costs and oversimplified contact interactions, making them impractical for real-world applications. In this work, we develop a real-time simulation and navigation control framework that integrates hard-magnetic elastic rod theory, formulated within the Discrete Differential Geometry (DDG) framework, with an order-reduced contact handling strategy. Our approach captures large deformations and complex interactions while maintaining computational efficiency. Next, the navigation control problem is formulated as an inverse design task, where optimal magnetic fields are computed in real time by minimizing the constrained forces and enhancing navigation accuracy. We validate the proposed framework through comprehensive numerical simulations and experimental studies, demonstrating its robustness, efficiency, and accuracy. The results show that our method significantly reduces computational costs while maintaining high-fidelity modeling, making it feasible for real-time deployment in clinical settings.

软体机器人磁控导航实时仿真医疗应用

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