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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This work is licensed under a 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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Citation Formats

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.