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Record W3128128049 · doi:10.1109/lra.2021.3057557

Design of a Reconfigurable Parallel Continuum Robot With Tendon-Actuated Kinematic Chains

2021· article· en· W3128128049 on OpenAlexaff
Georg Boettcher, Sven Lilge, Jessica Burgner-Kahrs

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKinematicsParallel manipulatorRobotModular designControl reconfigurationOrientation (vector space)Computer sciencePosition (finance)RepeatabilityRobot end effectorSimulationArtificial intelligenceControl theory (sociology)PhysicsGeometryMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

In this letter, a novel spatial parallel continuum robot is proposed. It is composed of three tendon-actuated continuum robots as kinematic chains that are coupled at a common end effector platform by spherical joints. A modular design approach is used, allowing the reconfiguration of each continuum robot's base position and orientation to adapt the overall structure to application specific constraints and environments. The parallel continuum robot is evaluated in terms of its position and orientation repeatability. Furthermore, comparisons are made to the performance of the single continuum kinematic chains. Overall, average position and orientation repeatabilities of 3.3 mm and 1.2° can be achieved for the parallel continuum robot, with a single individual continuum segment showing repeatabilities of 5.5 mm and 3.2°. The proposed parallel continuum robot exhibits improved repeatabilities than each of the employed individual continuum segments, improving the average repeatability by 67% for position and 167% for orientation, respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations45
Published2021
Admission routes1
Has abstractyes

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Same venueIEEE Robotics and Automation LettersSame topicSoft Robotics and ApplicationsFrench-language works237,207