Learning Friction Model for Magnet-actuated Tethered Capsule Robot
This work addresses control accuracy for medical diagnostic applications using capsule robots, representing an incremental improvement by applying a learning method to a known bottleneck.
The paper tackled the problem of accurately controlling a magnet-actuated tethered capsule robot by addressing the friction and drag forces that hinder motion, proposing a learning-based approach to model friction and demonstrating its effectiveness in real robot experiments.
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.