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Record W2792500139 · doi:10.1109/robio.2017.8324601

From the McGill pepper-mill carrier to the Kindai ATARIGI Carrier: A novel two limbs six-dof parallel robot with kinematic and actuation redundancy

2017· article· en· W2792500139 on OpenAlexaffabout
Takashi Harada, Jorge Angeles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsRedundancy (engineering)KinematicsJacobian matrix and determinantControl theory (sociology)Inverse kinematicsRobot kinematicsComputer scienceActuatorWorkspaceRobotEngineeringControl engineeringMathematicsArtificial intelligenceMobile robotPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

ATARIGI, a novel two limbs six-dof parallel robot, redundantly driven by eight actuators, is proposed in this paper. By the special arrangement of the mechanism, ATARIGI simultaneously has kinematic redundancy and actuation redundancy. Kinematic redundancy enables singularity avoidance and collision avoidance, that contribute to expanding the workspace. Actuation redundancy generates an internal force that eliminates backlash at the joints, thus increasing the positioning accuracy. Mechanical design of ATARIGI is extended from the McGill Pepper-Mill Carrier, a two limbs parallel Schönflies-motion generator. Inverse displacement analysis and Jacobian analysis of ATARIGI are derived. For utilization of the actuation redundancy and the kinematic redundancy, the null space of each Jacobian matrix is derived by means of computer algebra. The null spaces are simplified in closed form, which eases the control. Numerical tests demonstrate the collision-avoidance between limbs, while keeping the position and orientation of the gripper using the kinematic redundancy.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.573

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 studies0.0010.000
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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 teacher head, not a consensus.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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