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Record W1553969047 · doi:10.1109/isic.2002.1157743

Real-time tracking control with obstacle avoidance of multiple mobile robots

2003· article· en· W1553969047 on OpenAlex

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
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsObstacle avoidanceMobile robotTrajectoryObstacleController (irrigation)Computer scienceRobotControl theory (sociology)Tracking (education)Control (management)Path (computing)Vehicle dynamicsRobot controlControl engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

In this paper, a new method for real time tracking control with obstacle avoidance of multiple mobile robots is presented. The design goal was to track the desired trajectory safely and efficiently in a completely unknown environment. A novel tracking controller is proposed by incorporating neural dynamics equations into a conventional non-time based controller. An obstacle avoidance algorithm for the controller is also presented. The robots are able to pass around the obstacles toward the target without waiting for the removal of obstacles or replanning of robot path as in a conventional non-time based approach. In addition, the method is more efficient and less sensitive to sensor misreading. The effectiveness of the proposed method is demonstrated by a series of 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.

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: Methods · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score0.517

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 science0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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