Evidence map›Paper›PMID 42147458›Full record

ArticleIEEE robotics and automation letters2026

Friction Modeling of Tendon-driven Continuum Robots through Linear Complementarity Problem.

Jia Shen, Brendan Browne, Junhyoung Ha, Yue Chen

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jia ShenGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332 USA.
Brendan BrowneDepartment of Urology, Emory University School of Medicine, Atlanta, 30308 USA.
Junhyoung HaDepartment of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea.
Yue ChenInstitute for Robotics and Intelligent Machines and the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta 30332 USA.

Funding

Improved arrhythmia ablation via MR-guided robotic catheterization and multimodal clinician feedbackR01EB034359 · NIBIB · GEORGIA INSTITUTE OF TECHNOLOGY · PI Yue Chen, Aravindan Kolandaivelu · 2023 to 2026
$2.5M
NIBIB NIH HHS R01 EB034359
6 · The paper itself

Abstract

Tendon-driven continuum robots (TDCR) are widely used in medical interventions due to their inherent dexterity and compliance. However, precise motion planning and control of these robots remain challenging, largely because existing models do not accurately capture tendon frictional hysteresis. Predicting this friction-induced hysteresis has been recognized as an open problem due to the complex physics of tendon-disk interactions. In this letter, we propose a novel friction modeling approach for TDCR by incorporating the Capstan friction model as a set of complementarity constraints. Unlike conventional approaches that only predict the bounding effect of sliding friction, the proposed model captures the continuous change in tendon-disk friction forces and the transition between tendon sticking and sliding, thereby enabling the prediction of friction-induced hysteresis. The friction model is further formulated as the complementarity problem for convenient numerical implementation. We experimentally validated our approach on a simplified TDCR prototype and demonstrated a reduction of tip position error from 36.11 mm in conventional sliding friction models to 9.42 mm for a 402-mm-long robot. The source code is available at https://github.com/Jia0Shen/Friction-TDCR-Tension.

Indexed as

Friction ModelingHysteresisLinear Complementarity ProblemTendon-driven Continuum Robot

Identifiers

PMID42147458
PMCPMC13175125

What OpenQuestion holds

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Registered trials

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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.