Software toolkit for modeling, simulation and control of soft robots

Software toolkit for modeling, simulation and control of soft robots

E. Coevoet, T. Morales-Bieze, F. Largilliere, Z. Zhang, M. Thieffry, M. Sanz-Lopez,
B. Carrez, D. Marchal, O. Goury, J. Dequidt, and C. Duriez

 

 

This paper received the 6th Advanced Robotics Best Paper Award from the Robotics Society of Japan.

 

Abstract

The technological differences between traditional robotics and soft robotics have an impact on all of the modeling tools generally in use, including direct kinematics and inverse models, Jacobians, and dynamics. Due to the lack of precise modeling and control methods for soft robots, the promising concepts of using such design for complex applications (medicine, assistance, domestic robotics…) cannot be practically implemented. This paper presents a first unified software framework dedicated to modeling, simulation and control of soft robots. The framework relies on continuum mechanics for modeling the robotic parts and boundary conditions like actuators or contacts using a unified representation based on Lagrange multipliers. It enables the digital robot to be simulated in its environment using a direct model. The model can also be inverted online using an optimization-based method which allows to control the physical robots in the task space. To demonstrate the effectiveness of the approach, we present various soft robots scenarios including ones where the robot is interacting with its environment. The software has been built on top of SOFA, an open-source framework for deformable online simulation and is available at https://project.inria.fr/softrobot/

 

Advanced Robotics 2017, Pdf

@article{doi:10.1080/01691864.2017.1395362,
author = {E. Coevoet and T. Morales-Bieze and F. Largilliere and Z. Zhang and M. Thieffry and M. Sanz-Lopez and B. Carrez and D. Marchal and O. Goury and J. Dequidt and C. Duriez},
title = {Software toolkit for modeling, simulation, and control of soft robots},
journal = {Advanced Robotics},
year = {2017},
publisher = {Taylor & Francis},
doi = {10.1080/01691864.2017.1395362},
URL = { https://doi.org/10.1080/01691864.2017.1395362}}

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