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Record W2394585357 · doi:10.1109/tcst.2016.2562578

Synchronized Closed Path Following for a Differential Drive and Manipulator Robot

2016· article· en· W2394585357 on OpenAlexafffund
Yuqian Li, Christopher Nielsen

Bibliographic record

VenueIEEE Transactions on Control Systems Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSynchronizingControl theory (sociology)TraverseSynchronization (alternating current)Path (computing)TrajectoryRobotSerial manipulatorController (irrigation)Computer scienceDifferential (mechanical device)Robot kinematicsParallel manipulatorSingularityMotion planningControl engineeringMobile robotTopology (electrical circuits)EngineeringMathematicsControl (management)Artificial intelligenceTransmission (telecommunications)

Abstract

fetched live from OpenAlex

We locally solve a synchronized path-following problem for a heterogeneous multiagent system consisting of a differential drive robot and a serial manipulator. Each is assigned a simple, regular, and closed curve in its output space. The outputs of the systems must approach and traverse their assigned curves while synchronizing their motions along the paths. We use the notion of path-following outputs to facilitate a solution and present a novel synchronization controller and a novel singularity avoidance controller. The controllers are all given in closed form making their implementation straightforward. A numerical simulation is presented, which includes modeling uncertainty to demonstrate the utility of this approach.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.223
Teacher spread0.213 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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