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Learning Friction Model for Tethered Capsule Robot

2021/08/16 by Yi Wang, Yuchen He, Wang, Yi +9
Engineering · Medicine · #FOS: Computer and information sciences #Gastrointestinal Bleeding Diagnosis and Treatment #Platelet Disorders and Treatments #Robotics (cs.RO) #Soft Robotics and Applications

paper · pdf · doi:10.48550/arxiv.2108.07151

openalex publication_date 2021/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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