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Record W2120489973 · doi:10.1109/tro.2005.852254

Modeling and control of cooperative teleoperation systems

2005· article· en· W2120489973 on OpenAlexaff
Shahin Sirouspour

Bibliographic record

VenueIEEE Transactions on Robotics · 2005
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTeleoperationRobotTask (project management)TeleroboticsMaster/slaveControl engineeringControl (management)Stability (learning theory)Computer scienceArchitectureRobot kinematicsEngineeringSimulationMobile robotSystems engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a multilateral control architecture for teleoperation in multimaster/multislave environments. The proposed framework incorporates flow of position and force information between all master and slave robots, rather than merely between corresponding units. Within this architecture, cooperative performance measures are defined to enhance coordination among the operators and the robots for achieving the task objectives. A /spl mu/-synthesis-based methodology for cooperative teleoperation control is also introduced. This approach guarantees robust stability of cooperative teleoperation in the presence of dynamic interaction between slave robots, as well as unknown passive operators and environment dynamics. It also improves task coordination by optimizing relevant performance objectives. Experiments carried out with a two-master/two-slave single-axis system demonstrate the effectiveness of the proposed 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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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
GenreMethods

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

Citations159
Published2005
Admission routes1
Has abstractyes

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