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Improving the Cooling Time of Twisted Coiled Actuators in Soft Robotics

2024· article· en· W4403677830 on OpenAlexafffund
Alex Lizotte, Parisa Daemi, Brendan DiFabio, Ana Luisa Trejos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoft roboticsActuatorRoboticsArtificial intelligenceComputer sciencePneumatic actuatorMechanical engineeringControl engineeringRobotComputer visionEngineering

Abstract

fetched live from OpenAlex

Fabric cooling channels for twisted coiled actuators (TCAs) were recently proposed to achieve the required response times for motion assistance in a manner suitable for soft wearable robotic devices. While previous work demonstrated that the fabric channel reduced the cooling time by 42% in comparison to the same TCA without the cooling channel, the TCAs were still not cooled quickly enough to support human motion. Therefore, in this paper, two variations to the channel are proposed to further reduce the cooling time of the TCAs. The variations include unsealing the inlet and adding vents along the length of the channel to take advantage of air entrainment and natural convection. While both variations reduced the cooling time on their own, when they were employed together there was a 34% reduction in cooling time compared to the original channel design (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$19.1 \pm 2.4\ \mathrm{s}$</tex> vs. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$13.5 \pm 0.9\ \mathrm{s}, \mathrm{p} &lt; 0.001$</tex>). This decrease occurred without any significant differences in the stroke or heating time of the TCA. The modified channel was then compared to the TCA without the cooling apparatus and the cooling time was reduced by 57% (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$25.1 \pm 1.7\ \mathrm{s}$</tex> vs. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$14.0 \pm 1.2\ \mathrm{s}, \mathrm{p} &lt; 0.001)$</tex>. This work advances the development of a cooling system for TCAs, making it suitable for soft wearable robotic devices by improving portability, and thereby enabling their use in wearable devices for rehabilitation applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.210
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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