The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.
Sensing in Soft Robotics
Open accessAbstract
Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.
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Cites
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Cited by
Zhu, T., Wang, D., Wang, Y., Xu, F., Huang, J., Lian, M., Wang, Y., Fan, W., Miao, Y., Zhu, J., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202503693 · Cited by 32
Metadata
- Authors
- Hegde, C., Su, J., Tan, J. M. R., He, K., Chen, X., & Magdassi, S.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-08-02
- Volume
- 17
- Issue
- 16
- Pages
- 15277-15307
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W4385476004
- Cited by
- 384
- References
- 173
- FWCI
- 28.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Hegde, C., Su, J., Tan, J. M. R., He, K., Chen, X., & Magdassi, S. (2023). Sensing in Soft Robotics. ACS Nano. https://doi.org/10.1021/acsnano.3c04089
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
