The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Textile Technology for Soft Robotic and Autonomous Garments
Abstract
Textiles have emerged as a promising class of materials for developing wearable robots that move and feel like everyday clothing. Textiles represent a favorable material platform for wearable robots due to their flexibility, low weight, breathability, and soft hand‐feel. Textiles also offer a unique level of programmability because of their inherent hierarchical nature, enabling researchers to modify and tune properties at several interdependent material scales. With these advantages and capabilities in mind, roboticists have begun to use textiles, not simply as substrates, but as functional components that program actuation and sensing. In parallel, materials scientists are developing new materials that respond to thermal, electrical, and hygroscopic stimuli by leveraging textile structures for function. Although textiles are one of humankind's oldest technologies, materials scientists and roboticists are just beginning to tap into their potential. This review provides a textile‐centric survey of the current state of the art in wearable robotic garments and highlights metrics that will guide materials development. Recent advances in textile materials for robotic components (i.e., as sensors, actuators, and integration components) are described with a focus on how these materials and technologies set the stage for wearable robots programmed at the material level.
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Cites
Review of progress in shape-memory polymers
Liu, C., Qin, H., & Mather, P. T. (2007) · Journal of Materials Chemistry · doi:10.1039/b615954k · Cited by 1,942
Learning the signatures of the human grasp using a scalable tactile glove
Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162
Lederman, S. J., & Klatzky, R. L. (2009) · Attention, Perception & Psychophysics · doi:10.3758/app.71.7.1439 · Cited by 1,108
Human-powered wearable computing
Starner, T. (1996) · IBM Systems Journal · doi:10.1147/sj.353.0618 · Cited by 876
Wearable Haptic Systems for the Fingertip and the Hand: Taxonomy, Review, and Perspectives
Pacchierotti, C., Sinclair, S., Solazzi, M., Frisoli, A., Hayward, V., & Prattichizzo, D. (2017) · IEEE Transactions on Haptics · doi:10.1109/toh.2017.2689006 · Cited by 825
A soft robotic exosuit improves walking in patients after stroke
Awad, L. N., Bae, J., O’Donnell, K., De Rossi, S. M. M., Hendron, K., Sloot, L. H., Kudzia, P., Allen, S., Holt, K. G., Ellis, T. D., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aai9084 · Cited by 672
Self-citationMeshworm: 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
Self-citationIntegrated textile sensor patch for real-time and multiplex sweat analysis
He, W., Wang, C., Wang, H., Jian, M., Lu, W., Liang, X., Zhang, X., Yang, F., & Zhang, Y. (2019) · Science Advances · doi:10.1126/sciadv.aax0649 · Cited by 564
Machine-knitted washable sensor array textile for precise epidermal physiological signal monitoring
Fan, W., He, Q., Meng, K., Tan, X., Zhou, Z., Zhang, G., Yang, J., & Wang, Z. L. (2020) · Science Advances · doi:10.1126/sciadv.aay2840 · Cited by 516
Strain-programmable fiber-based artificial muscle
Kanik, M., Orguc, S., Varnavides, G., Kim, J., Benavides, T., Gonzalez, D., Akintilo, T., Tasan, C. C., Chandrakasan, A. P., Fink, Y., et al. (2019) · Science · doi:10.1126/science.aaw2502 · Cited by 468
Cited by
Fiber/Yarn and Textile-Based Piezoresistive Pressure Sensors
Yang, Y., Liu, Y., & Yin, R. (2025) · Advanced Fiber Materials · doi:10.1007/s42765-024-00479-5 · Cited by 110
Ultra-fine self-powered interactive fiber electronics for smart clothing
Wang, J., Yang, W., Liu, Z., Su, Y., Li, K., Li, Y., Zhang, Q., Hou, C., & Wang, H. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108171 · Cited by 44
KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting
Hofmann, M., Albaugh, L., Wang, T., Mankoff, J., & Hudson, S. E. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606789 · Cited by 28
Afsar, O. K., Pupillo, G., Vitucci, G., Babatain, W., Ishii, H., & Cacucciolo, V. (2026) · Science Robotics · doi:10.1126/scirobotics.ady6438 · Cited by 12
Metadata
- Authors
- Sanchez, V., Walsh, C. J., & Wood, R. J.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2020-12-10
- Volume
- 31
- Issue
- 6
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W3113191033
- Cited by
- 292
- References
- 15
- FWCI
- 10.10
- Type
- journal article
- Language
- en
- Open access
- closed
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