用混合模型与学习方法，精准估算手术机器人触觉力，误差低于10%。
A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots
- 融合模型与学习，先建模后补偿环境干扰
- 在腹腔模拟器上实现<10%的力估计误差
- 减少对全空间训练数据依赖，适用于多种柔性手术机器人
术中向主刀医生提供触觉反馈可提升手术安全性和沉浸感，但精确估算机器人手术器械尖端与组织的相互作用力仍具挑战。现有手术机器人大多无法直接测量作用力，额外传感器可能影响器械寿命。本文提出一种针对达芬奇研究套件（dVRK）患者侧机械臂（PSM）的混合模型与学习型力估计框架。基于模型的部分识别机器人动态参数并估计无负载时关节扭矩，基于学习的部分则补偿环境因素，如器械与穿刺套管间的摩擦附加扭矩。在腹腔模拟器上评估，力估计的归一化均方根误差低于10%。结果表明，通过模型方法进行动力学辨识，显著降低了对覆盖整个工作空间的训练数据的依赖。尽管专为dVRK设计，该方法具备通用性，可推广至其他柔顺型手术机器人。代码已开源：https://github.com/vu-maple-lab/dvrk_force_estimation。
原文摘要 · Abstract (English)
Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient's body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://github.com/vu-maple-lab/dvrk_force_estimation.
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