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Record W2105475590 · doi:10.1109/icma.2005.1626557

Uni-drive modular robots with pulse width modulation control

2006· article· en· W2105475590 on OpenAlexafffund
H. Karbasi, Amir Khajepour, Jan P. Huissoon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designRobotPulse-width modulationSelf-reconfiguring modular robotComputer scienceControl theory (sociology)Control engineeringClutchEngineeringRobot controlMobile robotControl (management)Artificial intelligenceElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

In this paper a newly developed concept and design in modular robots and relevant control methodology are introduced. This new class is called "uni-drive modular robots" whose design employs only a single drive for operating all the joints. The drive is mounted at the robot base and all joints tap power from a central rotating shaft inside each module using spring wrap clutches (SWC). This technique can effectively reduce the mass of each module which is the most important setback in design and applications of modular robots. From the control point of view, the uni-drive modular robot is controlled based upon the application of pulse width modulation (PWM) technique. By controlling the engagement time of the clutches, the position and velocity of the joints are regulated. In this work, we study the overall design of a planar uni-drive modular robot and obtain a self expansion formula which can automatically generate all equations of motion only based upon the number of modules. We further model SWCs and self locking mechanism which are parts of the proposed 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0000.000
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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

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