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Record W4220796196 · doi:10.20965/jaciii.2022.p0125

Synthesis and Prototyping of a 6-dof Parallel Robot for the Automatic Performance of Assembly Tasks

2022· article· en· W4220796196 on OpenAlexfundno aff
Jérôme Landuré, Clément Gosselin, Thierry Laliberté, Muhammad E. Abdallah

Bibliographic record

VenueJournal of Advanced Computational Intelligence and Intelligent Informatics · 2022
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of Canada
KeywordsComputer scienceRobotWorkspaceRobot controlRobot calibrationJacobian matrix and determinantRobot kinematicsController (irrigation)KinematicsRobot end effectorCartesian coordinate robotControl engineeringArtificial intelligenceSimulationMobile robotEngineering

Abstract

fetched live from OpenAlex

This paper presents the development of a 6-dof parallel robot for the performance of assembly tasks in a human-robot collaborative environment. The architecture and design of the robot are selected such that the robot is mechanically backdrivable. Thereby, the robot can physically interact with an environment or with humans without requiring the use of a force/torque sensor, which is the main objective of this work. The architecture of the robot is first described and its kinematic model is established. The Jacobian matrices are derived and an algorithm is presented for the determination of its workspace. The force capabilities of the robot are then established based on a proposed formulation. A prototype of the robot is presented and control schemes are developed, including a controller based on a vision system. Finally, a video demonstrating the experimental validation of the robot accompanies this paper. The video qualitatively demonstrates the interaction capabilities of the robot.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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