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Record W2067020902 · doi:10.1109/indin.2012.6301134

A mechatronics approach to design of path generators

2012· article· en· W2067020902 on OpenAlexafffund
Zhihong Sun, He Dong, Bing Zhang, Jian Huang, Wenjun Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRedundancy (engineering)ServomotorControl engineeringComputer scienceRobotMechatronicsMechanism designGenerator (circuit theory)Path (computing)Control theory (sociology)EngineeringArtificial intelligenceControl (management)Power (physics)Mathematics

Abstract

fetched live from OpenAlex

In a companion paper published elsewhere [1], we proposed a design approach based on the general redundancy concept to improve the dynamic performance of a mechanism, especially shaking moment and driving torque, while fulfilling the required task. The approach was based on a partial redundancy function of the servomotor, so the approach is called partial redundancy servomotor (PRSM). In this paper, we apply the PRSM to the path generator problem in robot design, in particular closed-loop multi-degrees of freedom robots or mechanisms. We demonstrate how the path generator design problem is solved and dynamic performance is improved in an integrated manner with the PRSM approach and how mechanism design and robot design are combined to design a better path generator. The contribution of this paper is the proposed PRSM design procedure. The other contribution is an integrated mechanism design and robot design approach that has implication to other general design problems such as function generator and posture generator.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.217
Teacher spread0.175 · 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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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