论文标题
用于手术机器人工具运动学控制的混合数据驱动和分析模型
Hybrid Data-Driven and Analytical Model for Kinematic Control of a Surgical Robotic Tool
论文作者
论文摘要
准确的运动学模型对于有效控制手术机器人至关重要。对于肌腱驱动的机器人，对于微创手术而言是常见的，内在的非线性很重要。传统的分析方法可以通过对非线性进行某些假设和简化来构建系统的运动学模型。相反，机器学习技术允许基于获得的数据恢复一个更复杂的模型。但是，分析模型更为普遍，但可以过度简化。另一方面，数据驱动的模型可以迎合更复杂的模型，但较不可能概括，结果受培训数据集的高度影响。在本文中，我们提出了一种结合分析和数据驱动方法的新方法，以模拟非线性肌腱驱动的手术机器人的运动学。高斯过程回归（GPR）用于学习数据驱动的模型，并在模拟数据和实际实验数据上测试了所提出的方法。
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which is common for minimally invasive surgery, intrinsic nonlinearities are important to consider. Traditional analytical methods allow to build the kinematic model of the system by making certain assumptions and simplifications on the nonlinearities. Machine learning techniques, instead, allow to recover a more complex model based on the acquired data. However, analytical models are more generalisable, but can be over-simplified; data-driven models, on the other hand, can cater for more complex models, but are less generalisable and the result is highly affected by the training dataset. In this paper, we present a novel approach to combining analytical and data-driven approaches to model the kinematics of nonlinear tendon-driven surgical robots. Gaussian Process Regression (GPR) is used for learning the data-driven model and the proposed method is tested on both simulated data and real experimental data.
