Planning Natural Locomotion for Articulated Soft Quadrupeds

Conference paper (2022)

Authors

Mathew Jose Pollayil University of Pisa
C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Deutsches Zentrum für Luft- und Raumfahrt (DLR)
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 Deutsches Zentrum für Luft- und Raumfahrt (DLR), Technische Universität München
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Mathew Jose Pollayil, C. Della Santina, George Mesesan, Johannes Englsberger, Daniel Seidel, Manolo Garabini, Christian Ott, Antonio Bicchi, Alin Albu-Schaffer
DOI: https://doi.org/10.1109/ICRA46639.2022.9812416
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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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- Embargo expired in 12-01-2023