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Record W2978352220 · doi:10.1109/lra.2019.2945468

Workspace Determination and Feedback Control of a Pick-and-Place Parallel Robot: Analysis and Experiments

2019· article· en· W2978352220 on OpenAlexaff
Bruno Belzile, Peyman Karimi Eskandary, Jorge Angeles

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsWrenchWorkspaceRobotTrajectorySMT placement equipmentGenerator (circuit theory)SimulationComputer scienceTracking (education)Control engineeringControl (management)EngineeringControl theory (sociology)Mechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The authors report on the analysis and experimental evaluation of a parallel Schönflies-motion generator (SMG) intended for fast pick-and-place operations and actuated with cylindrical drives (C-drives). Its CRRHHRRC isostatic architecture offers high rotability of the moving platform and a reduced number of limbs, as compared to robots available on the market. A simulation model using the dynamics of the robot as well as an experimental prototype are described. The results obtained are used to assess the pros and cons of two alternative C-drive architectures.A workspace analysis including the feasible wrenches is conducted. The industry standard Adept test cycle is used to evaluate the performance of the prototype. The data obtained prove that C-drives augmented with strain-wave gears give the SMG significantly better wrench capabilities and lower trajectory-tracking error, compared to their alternative C-drives based on cables.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.201
Teacher spread0.197 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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