Optimal path planning for unmanned ground vehicles using potential field method and optimal control method
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.
Bibliographic record
Abstract
This paper presents an optimal path planning algorithm for unmanned ground vehicle (UGV) to control its direction during parking manoeuvres by employing artificial potential field method (APF) combined with optimal control theory. A linear two-degree-of-freedom vehicle model with lateral and yaw motion is derived and simulated in MATLAB. The optimal control theory is employed to generate an optimal collision-free path For UGV from starting to the desired locations. The obstacle avoidance technique is mathematically modelled using APF including both the attractive and repulsive potential fields. The inclusion of these two potential fields ends up with a new potential field which is implemented to control the steering angle of the UGV to reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The results demonstrate the generated path for the UGV can satisfy vehicle dynamics constraints, avoid obstacles and reach the target location.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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 it