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Record W1653406877 · doi:10.1109/robot.2002.1014347

A non-time based tracking controller for multiple nonholonomic mobile robots

2003· article· en· W1653406877 on OpenAlexaff
Erhan Hu, Simon X. Yang, David Chiu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTeleoperationNonholonomic systemControl theory (sociology)Controller (irrigation)Mobile robotComputer scienceControl engineeringRobotTracking (education)AutomationMotion controlEvent (particle physics)EngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Event based controller design was first proposed by Xi (1993), where a suitable non-time motion reference is introduced to represent the desired and measurable system output. It has been successfully applied to many areas such as robot motion control, multirobot coordination, force and impact control, robotic teleoperation and manufacturing automation. In this paper, a non-time based tracking controller for multiple nonholonomic mobile robots is proposed by combining the conventional event based control technique with a biologically inspired shunting model first proposed by Grossberg (1982). The proposed control algorithm can generate smooth and continuous velocity control commands. These commands remove the small tracking error constraints in the conventional non-time based controllers. Thus, these controllers keep multiple robots in a required formation and coordinate in the presence of environment change. The effectiveness of the proposed controller is demonstrated by simulation and comparison studies.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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
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

Citations5
Published2003
Admission routes1
Has abstractyes

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