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Combining Thermoelectrics and Low Melting Point Alloys to Create Reconfigurable Stiff-Compliant Manipulators

2024· article· en· W4396875099 on OpenAlexfundno aff
Emily McCabe, Daniel S. Esser, Tayfun Efe Ertop, Alan Kuntz, Robert J. Webster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsRobotPayload (computing)WorkspaceMechanical engineeringComputer sciencePoint (geometry)State (computer science)Shape-memory alloyLink (geometry)Control engineeringSimulationMaterials scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Soft robots have garnered great interest in recent years due to their ability to navigate complex environments and enhance safety during unplanned collisions. However, their softness typically limits the forces they can apply and payloads they can carry, compared to traditional rigid-link robots. In this paper we seek to create a hybrid manipulator that can switch between a state in which it acts as a soft robot, and a state in which it has a series of selectively stiffenable links. The latter state, accomplished by solidifying chambers of low melting point metal alloy within the robot, is in some ways analogous to a traditional rigid-link manipulator. It also has the added benefit that each “link” can be set to a desired straight or curved shape before solidification and re-shaped when desired. Thermoelectric heat pumps enable local heating and cooling of the alloy, and tendons running along the robot enable actuation. Using a simple two-link prototype, we illustrate how alloy melting and solidification can be used to modify the robot's workspace and payload capacity.

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: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.446

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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