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Record W2873850312 · doi:10.1109/robosoft.2018.8404938

A paper-based wall-climbing robot enabled by electrostatic adhesion

2018· article· en· W2873850312 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsClimbSMA*RobotClimbingShape-memory alloyMaterials scienceAdhesionComputer scienceSimulationMechanical engineeringStructural engineeringEngineeringArtificial intelligenceComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, we report a paper-based wall-climbing robot capable of climbing vertical walls of different materials. The robot, made from paper and shape memory alloy (SMA), can be controlled to climb walls under certain combinations of activation patterns of electrostatic adhesion and contraction of the SMA. Electrostatic adhesion is applied using a paper structure with embedded interdigitated electrodes. This structure, fully compatible with the paper-based robot, is able to output strong and reliable adhesion forces (the resultant friction force can be as high as 1.65 N on specific substrates), and can be easily turned on and off using a commercial high-voltage converter. The SMA embedded in the robot is employed to deform the robot body and induce contracting displacements while being activated. The elastic energy stored in the robot body allows it to complete a repeatable actuation cycle by recovering the SMA automatically after its contraction. With above structures, we demonstrate the walking and climbing ability of this robot with a locomotion speed of 1 mm/s. The climbing of a vertical wall along both the vertical and horizontal directions is achieved.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.503

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 head0.215
Teacher spread0.208 · 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

Quick stats

Citations27
Published2018
Admission routes1
Has abstractyes

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