Learning Friction Model for Tethered Capsule Robot

TL;DR

This paper develops a learned friction model for a tethered capsule robot used in medical endoscopy, improving control accuracy by incorporating the friction dynamics into the system model.

Contribution

It introduces a method to learn the friction model from demonstrated trajectories, enhancing the control precision of capsule robots in medical applications.

Findings

Achieved a 5.6% reduction in tracking error with the learned friction model.

Built a tethered capsule robot system with magnetic actuation for experimental validation.

Demonstrated the effectiveness of the learned friction model in improving control accuracy.

Abstract

With the potential applications of capsule robots in medical endoscopy, accurate dynamic control of the capsule robot is becoming more and more important. In the scale of a capsule robot, the friction between capsule and the environment plays an essential role in the dynamic model, which is usually difficult to model beforehand. In the paper, a tethered capsule robot system driven by a robot manipulator is built, where a strong magnetic Halbach array is mounted on the robot's end-effector to adjust the state of the capsule. To increase the control accuracy, the friction between capsule and the environment is learned with demonstrated trajectories. With the learned friction model, experimental results demonstrate an improvement of 5.6% in terms of tracking error.