The DOI matches its Crossref record.
Soft Robotic Grippers for Biological Sampling on Deep Reefs
Open accessAbstract
Abstract This article presents the development of an underwater gripper that utilizes soft robotics technology to delicately manipulate and sample fragile species on the deep reef. Existing solutions for deep sea robotic manipulation have historically been driven by the oil industry, resulting in destructive interactions with undersea life. Soft material robotics relies on compliant materials that are inherently impedance matched to natural environments and to soft or fragile organisms. We demonstrate design principles for soft robot end effectors, bench-top characterization of their grasping performance, and conclude by describing in situ testing at mesophotic depths. The result is the first use of soft robotics in the deep sea for the nondestructive sampling of benthic fauna.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Biomedical 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
Design principles for creating synthetic underwater adhesives
Narayanan, A., Dhinojwala, A., & Joy, A. (2021) · Chemical Society Reviews · doi:10.1039/d1cs00316j · Cited by 193
OmniSkins: Robotic skins that turn inanimate objects into multifunctional robots
Booth, J. W., Shah, D., Case, J. C., White, E. L., Yuen, M. C., Cyr-Choiniere, O., & Kramer-Bottiglio, R. (2018) · Science Robotics · doi:10.1126/scirobotics.aat1853 · Cited by 156
Lessons for Robotics From the Control Architecture of the Octopus
Sivitilli, D. M., Smith, J. R., & Gire, D. H. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.862391 · Cited by 13
“This paper does not focus on actuation per se; this topic is covered thoroughly in reviews on soft robotics (Kim et al., 2013; Galloway et al., 2016; Manti et al., 2016; Polygerinos et al., 2017; Cianchetti et al., 2018; Whitesides, 2018).”
BackgroundAfsar, 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
- Galloway, K. C., Becker, K. P., Phillips, B., Kirby, J., Licht, S., Tchernov, D., Wood, R. J., & Gruber, D. F.
- Venue
- Soft Robotics
- Publisher
- SAGE Publications
- Published
- 2016-03
- Volume
- 3
- Issue
- 1
- Pages
- 23-33
- ISSN
- 2169-5172 (ISSN-L)
- 2169-5180
- OpenAlex
- W2296718990
- Cited by
- 848
- References
- 0
- FWCI
- 31.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Soft Robotic Grippers for Biological Sampling on Deep Reefs. https://app.cochat.ai/papers/10.1089/soro.2015.0019
- The paper
- Galloway, K. C., Becker, K. P., Phillips, B., Kirby, J., Licht, S., Tchernov, D., Wood, R. J., & Gruber, D. F. (2016). Soft Robotic Grippers for Biological Sampling on Deep Reefs. Soft Robotics. https://doi.org/10.1089/soro.2015.0019
Data sources
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.
