Learning to Navigate Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Hunter B. Gilbert, Faisal Mahmood, Nicholas J. Durr, Helder Araújo, Alp Eren Sari, Anurag Ajay
Abstract
Deep reinforcement learning (DRL) techniques have been successful in several domains, such as physical simulations, computer games, and simulated robotic tasks, yet the transfer of these successful learning concepts from simulations into the real world scenarios remains still a challenge. In this letter, a DRL approach is proposed to learn the continuous control of a magnetically actuated soft capsule endoscope (MASCE). Proposed controller approach can alleviate the need for tedious modeling of complex and highly nonlinear physical phenomena, such as magnetic interactions, robot body dynamics and tissue-robot interactions. Experiments performed in real ex-vivo porcine stomachs prove the successful control of the MASCE with trajectory tracking errors on the order of millimeter.
BibTeX
@inproceedings{ral2019_learningtonaviga,
title = {Learning to Navigate Endoscopic Capsule Robots},
author = {Mehmet Turan and Yasin Almalioglu and Hunter B. Gilbert and Faisal Mahmood and Nicholas J. Durr and Helder Araújo and Alp Eren Sari and Anurag Ajay and Metin Sitti},
booktitle = {RA-L 2019},
year = {2019}
}