Learning Friction Model for Magnet-actuated Tethered Capsule Robot

TL;DR

This paper introduces a learning-based friction model for a magnet-actuated tethered capsule robot, improving position control accuracy by accurately modeling environmental friction forces through real-world experiments.

Contribution

The paper presents a novel learning-based method to model friction forces in magnet-actuated tethered capsules, enhancing control precision in medical robotic applications.

Findings

The proposed model accurately predicts friction forces during capsule motion.

Real robot experiments demonstrate improved control accuracy.

The approach effectively accounts for complex environmental interactions.

Abstract

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.