Evidence map›Paper›PMID 40799461›Full record

ArticleFrontiers in robotics and AI2025

Parametric modeling of deformable linear objects for robotic outfitting and maintenance of space systems.

Amy Quartaro, Joshua Moser, John Cooper, Erik Komendera

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 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

4 authors.

Amy QuartaroFASER Lab, Virginia Tech, Mechanical Engineering Department, Blacksburg, VA, United States.
Joshua MoserAutonomous Integrated Systems Research Branch, NASA Langley Research Center, Hampton, VA, United States.
John CooperAutonomous Integrated Systems Research Branch, NASA Langley Research Center, Hampton, VA, United States.
Erik KomenderaFASER Lab, Virginia Tech, Mechanical Engineering Department, Blacksburg, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Outfitting and maintenance are important to an in-space architecture consisting of long duration missions. During such missions, crew is not continuously present; robotic agents become essential to the construction, maintenance, and servicing of complicated space assets, requiring some degree of autonomy to plan and execute tasks. There has been significant research into manipulation planning for rigid elements for in-space assembly and servicing, but flexible electrical cables, which fall under the domain of Deformable Linear Objects (DLOs), have not received such attention despite being critical components of powered space systems. Cables often have a non-zero bend equilibrium configuration, which the majority of DLO research does not consider. This article implements a model-based optimization approach to estimate cable configuration, where a design parameter of the model's discretization level enables trading model accuracy vs computational complexity. Observed 2D cable configurations are used to improve the model via parameter estimation. The parameter estimation is validated through comparing predicted configurations based on estimated parameters to that of a real cable. The incorporation of parameter estimation to the cable model is shown to reduce prediction errors by an order of magnitude. The results of this work demonstrate some of the challenges present with robotic cable manipulation, exploring the complexities of outfitting and maintenance operations of in-space facilities, and puts forth a method for reducing the size of the state space of a cable payload while accounting for non-zero equilibrium configurations.

Indexed as

deformable linear objectsin-space constructionlunar outfittingmodel based state estimationparameter estimationrobotic outfitting

Identifiers

PMID40799461
PMCPMC12340519

What OpenQuestion holds

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LicenceCC BY
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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.