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Position control of medical cable-driven flexible instruments by combining machine learning and kinematic analysis

机译：通过组合机器学习和运动学分析来定位医用电缆驱动的灵活仪器

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Non-linearities in cable transmissions are important limitations for the accurate control of flexible instruments used in medical endoscopic systems. Hysteresis effects greatly impact the accuracy of conventional kinematic models. This is especially critical for implementing automatic motions in flexible medical robotic systems. In this paper, we propose a method for improving open-loop accuracy of flexible instruments by implementing a Position Inverse Kinematic Model which is able to take into account hysteresis effects. In order to avoid complex physical modeling, the method relies on the off-line learning of the behavior of the instruments. Basic knowledge of the kinematic is also incorporated in the learning process in order to make it fast. The validity of the approach is demonstrated by the execution of 2D and 3D trajectories with the instruments of the STRAS medical robot. The accuracy is shown to be significantly improved with respect to other learning-based methods.
机译：电缆传输中的非线性是医疗内窥镜系统中使用的柔性仪器的准确控制的重要局限性。滞后效应极大地影响了传统运动模型的准确性。这对于在灵活医疗机器人系统中实施自动运动来说尤为重要。在本文中，我们提出了一种通过实现能够考虑滞后效应的位置逆运动学模型来提高灵活仪器的开环精度的方法。为了避免复杂的物理建模，该方法依赖于仪器行为的离线学习。运动学的基本知识也包含在学习过程中，以便快速。通过使用Stras医疗机器人的仪器执行2D和3D轨迹来证明该方法的有效性。关于基于学习的方法，显示了准确度明显改善。

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