Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten17293-17299
Seitenumfang7
ISBN (elektronisch)9798350384574
ISBN (Print)979-8-3503-8458-1
PublikationsstatusVeröffentlicht - 2024
Veranstaltung2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan
Dauer: 13 Mai 202417 Mai 2024

Publikationsreihe

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Abstract

Sophisticated models can accurately describe deformations of continuum robots while being computationally demanding, which limits their application. Especially when considering sampling-based path planning, the model has to be evaluated frequently, which can lead to substantially increased computation times. We present a new approach to compute the entire shape of a tendon-driven continuum robot by a physics-informed neural network (PINN). The underlying physics is modelled with the Cosserat rod theory and incorporated into the PINN's loss function. The boundary values for the training are obtained from a reference model, solved by the shooting method. Our approach allows for a computation of the learned Cosserat rod model multiple orders of magnitude faster than a publicly available reference model. The median position deviation from the reference model lies below 1mm (0.5% of the simulated robot length) for each of the robot's 20 disks.

ASJC Scopus Sachgebiete

Zitieren

Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory. / Bensch, Martin; Job, Tim David; Habich, Tim Lukas et al.
2024 IEEE International Conference on Robotics and Automation, ICRA 2024. Institute of Electrical and Electronics Engineers Inc., 2024. S. 17293-17299 (Proceedings - IEEE International Conference on Robotics and Automation).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Bensch, M, Job, TD, Habich, TL, Seel, T & Schappler, M 2024, Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory. in 2024 IEEE International Conference on Robotics and Automation, ICRA 2024. Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., S. 17293-17299, 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, Yokohama, Japan, 13 Mai 2024. https://doi.org/10.1109/ICRA57147.2024.10610742
Bensch, M., Job, T. D., Habich, T. L., Seel, T., & Schappler, M. (2024). Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory. In 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 (S. 17293-17299). (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA57147.2024.10610742
Bensch M, Job TD, Habich TL, Seel T, Schappler M. Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory. in 2024 IEEE International Conference on Robotics and Automation, ICRA 2024. Institute of Electrical and Electronics Engineers Inc. 2024. S. 17293-17299. (Proceedings - IEEE International Conference on Robotics and Automation). doi: 10.1109/ICRA57147.2024.10610742
Bensch, Martin ; Job, Tim David ; Habich, Tim Lukas et al. / Physics-Informed Neural Networks for Continuum Robots : Towards Fast Approximation of Static Cosserat Rod Theory. 2024 IEEE International Conference on Robotics and Automation, ICRA 2024. Institute of Electrical and Electronics Engineers Inc., 2024. S. 17293-17299 (Proceedings - IEEE International Conference on Robotics and Automation).
Download
@inproceedings{75453dbfc2a744be97e403c79af2a86e,
title = "Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory",
abstract = "Sophisticated models can accurately describe deformations of continuum robots while being computationally demanding, which limits their application. Especially when considering sampling-based path planning, the model has to be evaluated frequently, which can lead to substantially increased computation times. We present a new approach to compute the entire shape of a tendon-driven continuum robot by a physics-informed neural network (PINN). The underlying physics is modelled with the Cosserat rod theory and incorporated into the PINN's loss function. The boundary values for the training are obtained from a reference model, solved by the shooting method. Our approach allows for a computation of the learned Cosserat rod model multiple orders of magnitude faster than a publicly available reference model. The median position deviation from the reference model lies below 1mm (0.5% of the simulated robot length) for each of the robot's 20 disks.",
author = "Martin Bensch and Job, {Tim David} and Habich, {Tim Lukas} and Thomas Seel and Moritz Schappler",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 ; Conference date: 13-05-2024 Through 17-05-2024",
year = "2024",
doi = "10.1109/ICRA57147.2024.10610742",
language = "English",
isbn = "979-8-3503-8458-1",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "17293--17299",
booktitle = "2024 IEEE International Conference on Robotics and Automation, ICRA 2024",
address = "United States",

}

Download

TY - GEN

T1 - Physics-Informed Neural Networks for Continuum Robots

T2 - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024

AU - Bensch, Martin

AU - Job, Tim David

AU - Habich, Tim Lukas

AU - Seel, Thomas

AU - Schappler, Moritz

N1 - Publisher Copyright: © 2024 IEEE.

PY - 2024

Y1 - 2024

N2 - Sophisticated models can accurately describe deformations of continuum robots while being computationally demanding, which limits their application. Especially when considering sampling-based path planning, the model has to be evaluated frequently, which can lead to substantially increased computation times. We present a new approach to compute the entire shape of a tendon-driven continuum robot by a physics-informed neural network (PINN). The underlying physics is modelled with the Cosserat rod theory and incorporated into the PINN's loss function. The boundary values for the training are obtained from a reference model, solved by the shooting method. Our approach allows for a computation of the learned Cosserat rod model multiple orders of magnitude faster than a publicly available reference model. The median position deviation from the reference model lies below 1mm (0.5% of the simulated robot length) for each of the robot's 20 disks.

AB - Sophisticated models can accurately describe deformations of continuum robots while being computationally demanding, which limits their application. Especially when considering sampling-based path planning, the model has to be evaluated frequently, which can lead to substantially increased computation times. We present a new approach to compute the entire shape of a tendon-driven continuum robot by a physics-informed neural network (PINN). The underlying physics is modelled with the Cosserat rod theory and incorporated into the PINN's loss function. The boundary values for the training are obtained from a reference model, solved by the shooting method. Our approach allows for a computation of the learned Cosserat rod model multiple orders of magnitude faster than a publicly available reference model. The median position deviation from the reference model lies below 1mm (0.5% of the simulated robot length) for each of the robot's 20 disks.

UR - http://www.scopus.com/inward/record.url?scp=85202441470&partnerID=8YFLogxK

U2 - 10.1109/ICRA57147.2024.10610742

DO - 10.1109/ICRA57147.2024.10610742

M3 - Conference contribution

AN - SCOPUS:85202441470

SN - 979-8-3503-8458-1

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 17293

EP - 17299

BT - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 May 2024 through 17 May 2024

ER -

Von denselben Autoren