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Record W4408909487 · doi:10.18280/jesa.580216

Computation of Feasible Controls and Feasible States for a Four-Wheeled Autonomous Vehicle Robot

2025· article· fr· W4408909487 on OpenAlexvenueno aff
Masiala Mavungu, Daniel Mashao

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2025
Typearticle
Languagefr
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
FundersUniversity of Johannesburg
KeywordsComputationRobotComputer scienceControl engineeringSimulationControl theory (sociology)EngineeringArtificial intelligenceControl (management)Algorithm

Abstract

fetched live from OpenAlex

This research paper deals with computation of Feasible command strategies to guide a four-wheeled autonomous vehicle to move from a specified initial state to an eventual final state while minimizing a running cost.The problem is solved as follows: The vehicle is mathematically modeled as a non-linear system of seven ODEs with seven state and four command variables.The commands represent the controls.A costate system of seven ODEs is generated.The control's Feasibility conditions generate four constrained Feasible command strategies and each of them is defined as a function of states and costates.The state system and the costate system are rewritten accordingly.Such systems are then joined to give a nonlinear system of fourteen ODEs and an initial value problem.The results are the Feasible system response involving the robot path in the horizontal XY plane and the robot velocity, the costate functions, the command functions.Computational Simulations are developed and presented to summarize the results and to convince the readers on the accuracy.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.040
GPT teacher head0.296
Teacher spread0.256 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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