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Design of Dual-Master-Dual-Slave Teleoperation System through State Convergence

2019· article· en· W3034003194 on OpenAlexaff
Muhammad Usman Asad, Umar Farooq, Jason Gu, Valentina Emilia Bălaş, Marius M. Bǎlaş

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTeleoperationDual (grammatical number)Synchronization (alternating current)Control theory (sociology)Convergence (economics)Master/slaveComputer scienceState (computer science)MATLABTeleroboticsSimulationControl engineeringRobotControl (management)EngineeringMobile robotArtificial intelligence

Abstract

fetched live from OpenAlex

State convergence [15] and its extended version [17] are novel design methods which will assure the required dynamical system response and remove unwanted effects of the teleoperation systems modeled on the state space along with the synchronization of leader/follower devices. This paper discusses the scheme of dual-leader-dual-follower teleoperation system in detail. The design procedure requires 8n+4 parameters to be determined as compared to 3n+1 in case of single-leader-single-follower teleoperation system, where n is order of the leader/follower device. The feasibility of the proposed scheme to control a dual-leader-dual-follower teleoperation system over delay-less link is studied through simulations in MATLAB/Simulink environment. It is found that each master device can affect the motion of each slave device to a desired degree. Furthermore, the aim of getting requisite performance of the system also achieved while maintaining synchronization between leader/follower to common reference motion of all the leader devices.

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 categoriesInsufficient payload (model declined to judge)
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 score0.630
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.210
Teacher spread0.187 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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