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.

翻译：在机器人手术中向外科医生提供触觉反馈能够实现更安全、更沉浸式的手术，但估计机器人控制的手术器械末端的组织交互力已被证明具有挑战性。现有手术机器人鲜少能直接测量交互力，且额外传感器可能缩短器械使用寿命。本文提出一种用于达芬奇研究套件（dVRK）患者侧操纵器（PSM）的混合模型与学习驱动力量估计框架。模型驱动部分识别机器人动态参数并估计自由空间关节扭矩，而学习驱动部分则补偿环境因素，例如由PSM器械与患者体壁间套管交互产生的附加扭矩。我们在腹部仿真模型中对本方法进行评估，实现了归一化均方根误差低于10%的力量估计精度。研究表明，通过采用模型驱动方法进行动力学辨识，降低了对覆盖整个工作空间的训练数据的依赖。虽然本方法最初为dVRK开发，但所提出的框架可推广至其他柔性手术机器人。代码发布于https://github.com/vu-maple-lab/dvrk_force_estimation。