The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Soft Robotic Grippers
Open accessAbstract
Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end‐effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.
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Citations
Cites
Design, fabrication and control of soft robots
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Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review
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A reversible wet/dry adhesive inspired by mussels and geckos
Lee, H., Lee, B. P., & Messersmith, P. B. (2007) · Nature · doi:10.1038/nature05968 · Cited by 2,043
Review of progress in shape-memory polymers
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On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
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Highly stretchable electroluminescent skin for optical signaling and tactile sensing
Larson, C., Peele, B., Li, S., Robinson, S., Totaro, M., Beccai, L., Mazzolai, B., & Shepherd, R. (2016) · Science · doi:10.1126/science.aac5082 · Cited by 1,393
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Cited by
Wang, M., Luo, Y., Wang, T., Wan, C., Pan, L., Pan, S., He, K., Neo, A., & Chen, X. (2021) · Advanced Materials · doi:10.1002/adma.202003014 · Cited by 451
Yang, B., & Yuan, W. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01989 · Cited by 325
A dual-mode soft gripper for food packaging
Wang, Z., Or, K., & Hirai, S. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103427 · Cited by 222
High Force Density Textile Electrostatic Clutch
Hinchet, R., & Shea, H. (2020) · Advanced Materials Technologies · doi:10.1002/admt.201900895 · Cited by 132
Self-citationD’Avella, S., Tripicchio, P., & Avizzano, C. A. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101888 · Cited by 79
Metadata
- Authors
- Shintake, J., Cacucciolo, V., Floreano, D., & Shea, H.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2018-05-07
- Volume
- 30
- Issue
- 29
- Pages
- e1707035-e1707035
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2784648116
- Cited by
- 1,938
- References
- 24
- FWCI
- 50.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.