An experimental validation of the polynomial curvature model

identification and optimal control of a soft underwater tentacle

Journal Article (2022)
Author(s)

F. Stella (TU Delft - Learning & Autonomous Control, École Polytechnique Fédérale de Lausanne)

Nana Obayashi (École Polytechnique Fédérale de Lausanne)

C. Della Santina (TU Delft - Learning & Autonomous Control, Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Josie Hughes (École Polytechnique Fédérale de Lausanne)

Research Group
Learning & Autonomous Control
Copyright
© 2022 F. Stella, Nana Obayashi, C. Della Santina, Josie Hughes
DOI related publication
https://doi.org/10.1109/LRA.2022.3192887
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 F. Stella, Nana Obayashi, C. Della Santina, Josie Hughes
Research Group
Learning & Autonomous Control
Issue number
4
Volume number
7
Pages (from-to)
11410-11417
Reuse Rights

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Abstract

The control possibilities for soft robots have long been hindered by the lack of accurate yet computationally treatable dynamic models of soft structures. Polynomial curvature models propose a solution to this quest for continuum slender structures. Nevertheless, the results produced with this class of models have been so far essentially theoretical. With the present work, we aim to provide a much-needed experimental validation to these recent theories. To this end, we focus on soft tentacles immersed in water. First, we propose an extension of the affine curvature model to underwater structures, considering the drag forces arising from the fluid-solid interaction. Then, we extensively test the model's capability to describe the system behavior across several shapes and working conditions. Finally, we validate model-based control policies, proposing and solving an optimal control problem for directional underwater swimming. Using the model we show an average increase of more than 3.5 times the swimming speed of a sinusoidal baseline controller, with some tentacles showing an improvement in excess of 5.5 times the baseline.

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