提升机器人手术触觉反馈精度，减少操作时的突然反冲力。
Learning-based Force Sensing and Impedance Matching for Safe Haptic Feedback in Robot-assisted Laparoscopic Surgery
- 引入非线性阻抗匹配算法，实时建模器械与组织的复杂互动。
- 力反馈误差降低95%，均值绝对误差达0.01牛（标准差0.02）。
- 释放手柄时无反冲力，提升医生操作安全性和舒适度。
将精确的触觉反馈集成到机器人辅助微创手术（RAMIS）中仍面临挑战，主要源于力渲染精度不足及远程操作中的系统安全性问题。本文提出一种非线性阻抗匹配方法（NIMA），在先前验证过的阻抗匹配方法（IMA）基础上，引入非线性动态建模，以实现实时、精准地再现器械-组织复杂交互。NIMA实现了0.01牛（标准差0.02牛）的均方绝对误差，相较IMA降低95%。同时，通过确保用户释放操纵手柄时施加于手部的力为零，彻底消除触觉“反冲”现象，显著提升了患者安全与操作者舒适度。通过对器械-组织相互作用中非线性的有效建模，NIMA大幅提升了力反馈的保真度、响应速度与精度，适用于多种外科场景，推动了可靠机器人辅助手术触觉系统的进步。
原文摘要 · Abstract (English)
Integrating accurate haptic feedback into robot-assisted minimally invasive surgery (RAMIS) remains challenging due to difficulties in precise force rendering and ensuring system safety during teleoperation. We present a Nonlinear Impedance Matching Approach (NIMA) that extends our previously validated Impedance Matching Approach (IMA) by incorporating nonlinear dynamics to accurately model and render complex tool-tissue interactions in real-time. NIMA achieves a mean absolute error of 0.01 (std 0.02 N), representing a 95% reduction compared to IMA. Additionally, NIMA eliminates haptic "kickback" by ensuring zero force is applied to the user's hand when they release the handle, enhancing both patient safety and operator comfort. By accounting for nonlinearities in tool-tissue interactions, NIMA significantly improves force fidelity, responsiveness, and precision across various surgical conditions, advancing haptic feedback systems for reliable robot-assisted surgical procedures.
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