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Design of Lightweight and Extensible Tendon-Driven Continuum Robots using Origami Patterns

2021· article· en· W3182117585 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 institutionsUniversity of Toronto
Fundersnot available
KeywordsExtensibilityRobotWorkspaceComputer scienceScalabilityProcess (computing)SimulationArtificial intelligence

Abstract

fetched live from OpenAlex

Tendon-driven continuum robots (TDCR) have been researched to utilize their slender structure, compliance, large workspace, and follow-the-leader deployment capabilities. Improving the performances of TDCR by increasing its length and extensibility is however challenging due to the need of guiding the tendons along the backbone. A standard design uses rigid spacer disks, which mass may cause stability issues in the case of robots with long length. In this paper, we propose a design of lightweight and extensible TDCR taking advantage of extensible paper-based origami structures to guide the tendons along a superelastic nitinol backbone. The four tendons and the backbone control the three degrees of freedom of the robot, yaw, pitch, and axial translation. The robot is composed of multi-segments, and the manufacturing process with water-based ink printing provides fast, easily customizable, and scalable fabrication of the robots. The design allows for a reduction of up to 95% of the robot mass with respect to a standard design of TDCR. The prototype demonstrates the extension ratio of more than 10 times and ±167 degrees yaw/pitch angle with 21.9 g weight. The proposed robot design can be applied for search and rescue missions and minimally invasive surgical 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.

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 score0.732
Threshold uncertainty score0.302

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 head0.027
GPT teacher head0.232
Teacher spread0.204 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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