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Record W2154931045 · doi:10.1109/acc.2011.5990747

A new bilaterally teleoperated robotic vehicle platform with passivity control

2011· article· en· W2154931045 on OpenAlexaff
J. Ware, Ya‐Jun Pan, Trent Hilliard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTeleoperationPassivitySteering wheelComputer scienceSoftwareTeleroboticsSimulationInterface (matter)Channel (broadcasting)Master/slaveEmbedded systemEngineeringControl engineeringRobotMobile robotAutomotive engineeringElectrical engineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new bilateral teleoperated vehicle testing platform is proposed. In the platform, we apply a new Power-based Time Domain Passivity Control (PTDPC) method in an effort to improve the transparency. The platform is realized where steering angle commands were transmitted from a steering wheel interface to a remote vehicle, and road surface forces are sent back from the vehicle to the steering wheel interface. The communication channel between the steering wheel (master side) and the remote vehicle (slave side) introduces time varying delays in the transmitted signals. To ensure the stability, PTDPCs are applied to each side of the communication channel, which further improve the tracking performance as well and reduce the overall effort required of the human operator. The alternative advantage is the simplicity: the dynamic models of both master and slave side systems are not required to be known. The algorithms, hardware and software realizations are described thoroughly and experimental results are demonstrated to show the effectiveness of the proposed approach as well as the functionality of the platform.

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: Empirical
Teacher disagreement score0.455
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)0.0010.000

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 head0.017
GPT teacher head0.173
Teacher spread0.156 · 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
Published2011
Admission routes1
Has abstractyes

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