Computation of Feasible Controls and Feasible States for a Four-Wheeled Autonomous Vehicle Robot
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".