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.
Details
- Organisationseinheit(en)
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Institut für Mechatronische Systeme
- Typ
- Aufsatz in Konferenzband
- Seiten
- 17293-17299
- Anzahl der Seiten
- 7
- Publikationsdatum
- 2024
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Software, Steuerungs- und Systemtechnik, Elektrotechnik und Elektronik, Artificial intelligence
- Elektronische Version(en)
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https://doi.org/10.1109/ICRA57147.2024.10610742 (Zugang:
Geschlossen
)