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Record W2152785709 · doi:10.1109/iros.2000.895210

Neural network control of mobile manipulators

2002· article· en· W2152785709 on OpenAlex
Sheng Lin, A.A. Goldenberg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Convergence (economics)Artificial neural networkComputer scienceKinematicsController (irrigation)Mobile manipulatorMotion controlStability (learning theory)Control engineeringRobust controlBounded functionMobile robotEstimatorControl (management)Control systemArtificial intelligenceEngineeringRobotMathematicsMachine learning

Abstract

fetched live from OpenAlex

In this paper, a novel neural-net (NN) based control methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be unknown and is to be identified by the NN online estimators. No preliminary learning stage of NN weight matrices is required. The controller is capable of disturbance-rejection in the presence of unknown bounded disturbances. Closed-loop stability of the control system and convergence of the NN learning processes are both guaranteed. Experimental tests on a two-DOE manipulator arm illustrate that the proposed control is significantly better than conventional robust control.

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.

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: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.653

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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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