An experimental validation of the polynomial curvature model: identification and optimal control of a soft underwater tentacle

Francesco Stella, Nana Obayashi, Cosimo Della Santina, Josie Hughes

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Pages (from-to)11410-11417
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Analytical models
  • Computational modeling
  • Flexible Robotics
  • Kinematics
  • Modeling, Control, and Learning for Soft Robots
  • Robots
  • Soft robotics
  • Strain
  • System identification
  • System Identification

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