Evidence map›Paper›PMID 36733442›Full record

ArticleIEEE robotics and automation letters2023

Towards A Physics-based Model for Steerable Eversion Growing Robots.

Zicong Wu, Mikel De Iturrate Reyzabal, S M Hadi Sadati, Hongbin Liu, Sebastien Ourselin, Daniel Leff, Robert K Katzschmann, Kawal Rhode, Christos Bergeles

Open access · greenAbstract read
In one paragraph

Article in IEEE robotics and automation letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Tip-Growing Robots: Design, Theory, Application.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Zicong WuSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Mikel De Iturrate ReyzabalSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
S M Hadi SadatiSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Hongbin LiuSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Sebastien OurselinSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Daniel LeffFaculty of Medicine, Department of Surgery & Cancer, and the Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK.
Robert K KatzschmannSoft Robotics Lab, ETH Zurich, Switzerland.
Kawal RhodeSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Christos BergelesSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
King's College London · GBImperial College London · GB

Funding

Cancer Research UK 30051European Research Council 714562Wellcome Trust 203148
6 · The paper itself

Abstract

Soft robots that grow through eversion/apical extension can effectively navigate fragile environments such as ducts and vessels inside the human body. This paper presents the physics-based model of a miniature steerable eversion growing robot. We demonstrate the robot's growing, steering, stiffening and interaction capabilities. The interaction between two robot-internal components is explored, i.e., a steerable catheter for robot tip orientation, and a growing sheath for robot elongation/retraction. The behavior of the growing robot under different inner pressures and external tip forces is investigated. Simulations are carried out within the SOFA framework. Extensive experimentation with a physical robot setup demonstrates agreement with the simulations. The comparison demonstrates a mean absolute error of 10 - 20% between simulation and experimental results for curvature values, including catheter-only experiments, sheath-only experiments and full system experiments. To our knowledge, this is the first work to explore physics-based modelling of a tendon-driven steerable eversion growing robot. While our work is motivated by early breast cancer detection through mammary duct inspection and uses our MAMMOBOT robot prototype, our approach is general and relevant to similar growing robots.

Identifiers

PMID36733442
PMCPMC7614130
OpenAlexW4313598773

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.