Planning Natural Locomotion for Articulated Soft Quadrupeds

Conference Paper (2022)
Author(s)

Mathew Jose Pollayil (University of Pisa)

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

George Mesesan (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Johannes Englsberger (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Daniel Seidel (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Manolo Garabini (University of Pisa)

Christian Ott (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Antonio Bicchi (University of Pisa)

Alin Albu-Schaffer (Technische Universität München, Deutsches Zentrum für Luft- und Raumfahrt (DLR))

DOI related publication
https://doi.org/10.1109/ICRA46639.2022.9812416 Final published version
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Publication Year
2022
Language
English
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.
Pages (from-to)
6593-6599
Publisher
IEEE
ISBN (print)
978-1-7281-9680-0
ISBN (electronic)
978-1-7281-9681-7
Event
Downloads counter
335
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Abstract

Embedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot.

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