The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Software toolkit for modeling, simulation, and control of soft robots
Open accessAbstract
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, etc.) 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 and 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/.
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Cites
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
Soft robotics: a bioinspired evolution in robotics
Kim, S., Laschi, C., & Trimmer, B. (2013) · Trends in Biotechnology · doi:10.1016/j.tibtech.2013.03.002 · Cited by 2,092
Cited by
Soft Robots Modeling: A Structured Overview
Armanini, C., Boyer, F., Mathew, A. T., Duriez, C., & Renda, F. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3231360 · Cited by 336
Self-citationFinite Element Modeling of Soft Fluidic Actuators: Overview and Recent Developments
Xavier, M. S., Fleming, A. J., & Yong, Y. K. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202000187 · Cited by 313
Challenges and Outlook in Robotic Manipulation of Deformable Objects
Zhu, J., Cherubini, A., Dune, C., Navarro-Alarcon, D., Alambeigi, F., Berenson, D., Ficuciello, F., Harada, K., Kober, J., Li, X., et al. (2022) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2022.3147415 · Cited by 265
Machine Learning for Soft Robotic Sensing and Control
Chin, K., Hellebrekers, T., & Majidi, C. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.201900171 · Cited by 215
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
Li, C., Xia, F., Martín-Martín, R., Lingelbach, M., Srivastava, S., Shen, B., Vainio, K., Gokmen, C., Dharan, G., Jain, T., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.03272 · Cited by 58
Metadata
- Authors
- Coevoet, E., Morales-Bieze, T., Largilliere, F., Zhang, Z., Thieffry, M., Sanz-Lopez, M., Carrez, B., Marchal, D., Goury, O., Dequidt, J., & Duriez, C.
- Venue
- Advanced Robotics
- Publisher
- Informa UK Limited
- Published
- 2017-11-17
- Volume
- 31
- Issue
- 22
- Pages
- 1208-1224
- ISSN
- 0169-1864 (ISSN-L)
- 1568-5535
- OpenAlex
- W2768326939
- Cited by
- 244
- References
- 2
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Coevoet, E., Morales-Bieze, T., Largilliere, F., Zhang, Z., Thieffry, M., Sanz-Lopez, M., Carrez, B., Marchal, D., Goury, O., Dequidt, J., & Duriez, C. (2017). Software toolkit for modeling, simulation, and control of soft robots. Advanced Robotics. https://doi.org/10.1080/01691864.2017.1395362
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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