The potential diagnostic applications of magnet-actuated capsules have been greatly increased in recent years. For most of these potential applications, accurate position control of the capsule have been highly demanding. However, the friction between the robot and the environment as well as the drag force from the tether play a significant role during the motion control of the capsule. Moreover, these forces especially the friction force are typically hard to model beforehand. In this paper, we first designed a magnet-actuated tethered capsule robot, where the driving magnet is mounted on the end of a robotic arm. Then, we proposed a learning-based approach to model the friction force between the capsule and the environment, with the goal of increasing the control accuracy of the whole system. Finally, several real robot experiments are demonstrated to showcase the effectiveness of our proposed approach.
翻译：近年来,磁活性胶囊的潜在诊断应用大为增加。对于大多数这些潜在应用,对胶囊的准确位置控制要求很高。然而,机器人与环境之间的摩擦以及绳系的拖力在胶囊运动控制期间起着重要作用。此外,这些力量,特别是摩擦力通常很难事先建模。在本文中,我们首先设计了一个磁活性绳索胶囊机器人,在机器人臂的末端安装了驱动磁铁。然后,我们提出了一种基于学习的方法来模拟胶囊与环境之间的摩擦力,目的是提高整个系统的控制准确性。最后,一些真正的机器人实验展示了我们拟议方法的有效性。
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