The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
Open accessAbstract
From a functional standpoint, classic robots are not at all similar to biological systems. If compared with rigid robots, animals’ bodies look overly redundant, imprecise, and weak. Nevertheless, animals can still perform a vast range of activities with unmatched effectiveness. Many studies in biomechanics have pointed to the elastic and compliant nature of the musculoskeletal system as a fundamental ingredient explaining this gap. Thus, to reach performance comparable to nature, elastic elements have been introduced in rigid-bodied robots, leading to articulated soft robotics[1](see “Summary”). In continuum soft robotics, this concept is brought to an extreme. Here, softness is not concentrated at the joint level but instead distributed across the whole structure. As a result, soft robots (henceforth, omitting the adjectivecontinuum) are entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and the soft appendices of vertebrate animals (for example, an elephant’s trunk and the tail of a monkey). Several soft robotic hardware platforms have been proposed with increasingly higher reliability and functionalities. In this process, considerable attention has been devoted to the technological side of the problem, leading to a large assortment of hardware solutions. In turn, this abundance opened up the challenge of developing effective control strategies that can manage the soft body and exploit its embodied intelligence.
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Citations
Cites
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
Self-citationDomain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124 · cited 1×
“Models developed from first principles can be used to create virtual environments where robots can evolve [221], [222] or learn new skills [93], [223], [224].”
BackgroundSoft 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
Soft robotics: Technologies and systems pushing the boundaries of robot abilities
Laschi, C., Mazzolai, B., & Cianchetti, M. (2016) · Science Robotics · doi:10.1126/scirobotics.aah3690 · Cited by 1,570 · cited 1×
“Of the many review articles about soft robotics [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], only [11] is focused on the control challenge, which does not follow a model-based approach.”
BackgroundBiomedical applications of soft robotics
Cianchetti, M., Laschi, C., Menciassi, A., & Dario, P. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0022-y · Cited by 1,435
Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080
Meshworm: A Peristaltic Soft Robot With Antagonistic Nickel Titanium Coil Actuators
Seok, S., Onal, C. D., Cho, K.-J., Wood, R. J., Rus, D., & Kim, S. (2013) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2012.2204070 · Cited by 665 · cited 1×
“Crawling motions of soft worms have been improved via ILC in [214], [215].”
BackgroundSelf-citation
Metadata
- Authors
- Della Santina, C., Duriez, C., & Rus, D.
- Venue
- IEEE Control Systems
- Published
- 2023-06
- Volume
- 43
- Issue
- 3
- Pages
- 30-65
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W3204719635
- Cited by
- 402
- References
- 7
- FWCI
- 27.14
- Type
- journal article
- Language
- en
- Open access
- green
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