Learning Friction Model for Tethered Capsule Robot
This addresses the challenge of dynamic control for capsule robots in medical applications, though it is incremental as it builds on existing methods with a specific enhancement.
The paper tackled the problem of accurately controlling a tethered capsule robot for medical endoscopy by learning the friction between the capsule and the environment from demonstrated trajectories, resulting in a 5.6% improvement in tracking error.
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.