Shape Sensing of Continuum Manipulators with Fiber Bragg Grating Sensor Arrays: Accounting for Actuator Velocity Effects.

Golchehr Amirkhani, Xinran Liu, Mehran Armand

Abstract read

In one paragraph

Article in IEEE sensors journal, 2025. 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

3 authors.

Golchehr AmirkhaniDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA, and with the Laboratory of Biomechanical and Image Guided Surgical Systems, Johns Hopkins University, Baltimore, MD, 21218 USA.ORCID 0000-0002-1743-1750

Xinran LiuDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA, and also with the Laboratory of Biomechanical and Image Guided Surgical Systems, Johns Hopkins University, Baltimore, MD, 21218 USA.ORCID 0000-0003-1028-8303

Mehran ArmandDepartment of Mechanical Engineering, and Laboratory of Biomechanical and Image Guided Surgical Systems, Johns Hopkins University, Baltimore, MD, 21218 USA, and Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA, and also with the Institute for Integrative and Innovative Research (IR) and Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR 72701.

Funding

Image-Guided Workstation and Tools for the Treatment of Bone DefectsR01EB016703 · NIBIB · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN · 2013 to 2022

$3.4M

Robotic System for Spinal Decompression and Interbody FusionR01AR080315 · NIAMS · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN, JAIN, AMIT · 2022 to 2025

$1.7M

NIAMS NIH HHS R01 AR080315NIBIB NIH HHS R01 EB016703

6 · The paper itself

Abstract

Tracking and controlling the shape of continuum dexterous manipulators (CDMs) in real time during non-constant curvature bending and straightening is still a challenging task. This study introduces a velocity-dependent shape reconstruction model using fiber Bragg grating (FBG) sensors, addressing velocity-dependent curvature discrepancies caused by the sensing unit sliding within the manipulator's channel. By incorporating these effects, the proposed model improves shape tracking accuracy across different velocities. Experimental validation in free and constrained environments at actuator velocities of 0.2, 0.8, and 1.4

Indexed as

continuum manipulatorFiber Bragg grating (FBG)large curvatureminimally invasive robotic surgeryvelocity-dependent shape reconstruction model

Identifiers

PMID42100500

DOI10.1109/jsen.2025.3558485

PMCPMC13148279

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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.