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Learning to Navigate Endoscopic Capsule Robots
Date
2019-07-01
Author
Turan, Mehmet
Almalioglu, Yasin
Gilbert, Hunter B.
Mahmood, Faisal
Durr, Nicholas J.
Araujo, Helder
Sari, Alp Eren
Ajay, Anurag
Sitti, Metin
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Artificial Intelligence
,
Computer Vision and Pattern Recognition
,
Computer Science Applications
URI
https://hdl.handle.net/11511/68516
Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
DOI
https://doi.org/10.1109/lra.2019.2924846
Collections
Department of Electrical and Electronics Engineering, Article
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M. Turan et al., “Learning to Navigate Endoscopic Capsule Robots,”
IEEE ROBOTICS AND AUTOMATION LETTERS
, pp. 3075–3082, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68516.