Wang Yuan, Dupont Pierre E
Department of Cardiac Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
IEEE Robot Autom Lett. 2025 Jan;10(1):96-103. doi: 10.1109/lra.2024.3504321. Epub 2024 Nov 21.
While robotic control of catheter motion can improve tip positioning accuracy, hysteresis arising from tendon friction and flexural deformation degrades kinematic modeling accuracy. In this paper, we compare the capabilities of three types of models for representing the forward and inverse kinematic maps of a clinical single-tendon cardiac catheter. Classical hysteresis models, neural networks and hybrid combinations of the two are included. Our results show that modeling accuracy is best when models are trained using motions corresponding to the anticipated clinical motions. For sinusoidal motions, recurrent neural network models provide the best performance. For point-to-point motions, however, a simple backlash model can provide comparable performance to a recurrent neural network.
虽然导管运动的机器人控制可以提高尖端定位精度，但肌腱摩擦和弯曲变形引起的滞后会降低运动学建模精度。在本文中，我们比较了三种类型的模型在表示临床单肌腱心脏导管的正向和反向运动学映射方面的能力。其中包括经典滞后模型、神经网络以及两者的混合组合。我们的结果表明，当使用与预期临床运动相对应的运动对模型进行训练时，建模精度最佳。对于正弦运动，递归神经网络模型表现最佳。然而，对于点对点运动，一个简单的间隙模型可以提供与递归神经网络相当的性能。
