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Record W2000839790 · doi:10.1017/s0263574711000075

Trajectory tracking compensation for teleoperation with transmission delays

2011· article· en· W2000839790 on OpenAlexaff
Lingyan Hu, Shuai Liu, Shaoping Xu

Bibliographic record

VenueRobotica · 2011
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsTeleoperationControl theory (sociology)PassivityTrajectoryComputer scienceMaster/slaveCompensation (psychology)Rendering (computer graphics)Tracking (education)SimulationRobotEngineeringComputer visionArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

SUMMARY The proper transmission of wave variables rather than power variables in teleoperation with time delays ensures system passivity – rendering the entire system stable, but the introduction of wave variables leads to distortion between the velocities/positions of the master and slave, and the performance deteriorates significantly with the increase of time delays. This paper presents a new compensating scheme implemented at the slave side to remove or cancel partially the distortion. The system passivity can still be maintained by tuning a properly designed low-pass filter. Compared with previous work, the main contribution of this work exists in two points: first, it is the actual velocity/trajectory of the slave rather than the reference velocity/trajectory of the slave that tracks the master one, so the quality of tracking is better and converges faster; second, the proposed compensator does not require any additional signal from the master side, minimizing the amount of data transmitted over the channel, which is very important from the practical point of view. The simulation and experiment results show that the velocity (or trajectory) tracking is significantly improved under the condition of stability, and the force presented to the user is close to the environment one, meaning a high degree of system transparency is achieved.

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 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 score0.941
Threshold uncertainty score0.335

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)0.0000.000

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 head0.040
GPT teacher head0.210
Teacher spread0.170 · 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.

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
Published2011
Admission routes1
Has abstractyes

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