MétaCan
Menu
Back to cohort
Record W2530936215 · doi:10.11159/cdsr16.102

Modeling and Motion Control of a Magnetically Navigated Microrobotic System

2016· article· en· W2530936215 on OpenAlexaff
Xiaodong Zhang, Mir Behrad Khamesee

Bibliographic record

VenueProceedings of the International Conference of Control, Dynamic systems, and Robotics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMotion controlMotion (physics)Computer scienceComputer visionArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

This paper presents a technology for remotely navigating a magnetized microrobot in three-degree-of-freedom (3-DOF) using magnetic energy. The magnetic energy source is a magnetic drive unit that consists of six iron-core electromagnets, a soft-iron yoke, and a soft-iron pole-piece. The dynamic models of the magnetically navigated microrobot were derived from the magnetic field model in the workspace. An experimental method was applied to define the magnetic field model. Based on the derived dynamic models, a feed-forward plus standard PID controller was used to control the motion of the microrobot with 3-DOF. Experiment was conducted to validate the performance of the proposed controller. The experimental results showed 20m motion accuracy in 3-DOF in a motion range of 10 10 30mm 3 .

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference of Control, Dynamic systems, and RoboticsSame topicMicro and Nano RoboticsFrench-language works237,207