Evidence map›Paper›PMID 36544557›Full record

ArticleIEEE robotics and automation letters2022

Enhanced Accuracy in Magnetic Actuation: Closed-loop Control of a Magnetic Agent with Low-Error Numerical Magnetic Model Estimation.

Onder Erin, Suraj Raval, Trevor J Schwehr, Will Pryor, Yotam Barnoy, Adrian Bell, Xiaolong Liu, Lamar O Mair, Irving N Weinberg, Axel Krieger and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE robotics and automation letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

11 authors.

Onder ErinMechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Suraj RavalMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA.
Trevor J SchwehrMechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Will PryorComputer Science Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Yotam BarnoyComputer Science Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Adrian BellMechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Xiaolong LiuMechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Lamar O MairDivision of Magnetic Manipulation & Particle Research, Weinberg Medical Physics, Inc., North Bethesda, MD 20852, USA.
Irving N WeinbergDivision of Magnetic Manipulation & Particle Research, Weinberg Medical Physics, Inc., North Bethesda, MD 20852, USA.
Axel KriegerMechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Yancy Diaz-MercadoMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA.

Funding

Next Generation of Surgical Imaging and Robotics for Supervised Autonomous Soft Tissue SurgeryR01EB020610 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI HSIEH, MICHAEL HARRISON, KANG, JIN U · 2016 to 2019
$1.5M
NIBIB NIH HHS R01 EB020610
6 · The paper itself

Abstract

Magnetic actuation holds promise for wirelessly controlling small, magnetic surgical tools and may enable the next generation of ultra minimally invasive surgical robotic systems. Precise torque and force exertion are required for safe surgical operations and accurate state control. Dipole field estimation models perform well far from electromagnets but yield large errors near coils. Thus, manipulations near coils suffer from severe (10×) field modeling errors. We experimentally quantify closed-loop magnetic agent control performance by using both a highly erroneous dipole model and a more accurate numerical magnetic model to estimate magnetic forces and torques for any given robot pose in 2D. We compare experimental measurements with estimation errors for the dipole model and our finite element analysis (FEA) based model of fields near coils. With five different paths designed for this study, we demonstrate that FEA-based magnetic field modeling reduces positioning root-mean-square (RMS) errors by 48% to 79% as compared with dipole models. Models demonstrate close agreement for magnetic field direction estimation, showing similar accuracy for orientation control. Such improved magnetic modelling is crucial for systems requiring robust estimates of magnetic forces for positioning agents, particularly in force-sensitive environments like surgical manipulation.

Indexed as

Closed-loop ControlMagnetic ModelingMagnetic RobotsMedical Robotics

Identifiers

PMID36544557
PMCPMC9762677

What OpenQuestion holds

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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.