PID Controller Enhanced A* Algorithm for Efficient Water Boat
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
The integration of a PID controller into the A* algorithm presents a novel approach to enhance water boat path planning efficiency.This fusion leverages the precision of the PID controller to fine-tune the navigation decisions made by the A* algorithm, optimizing trajectory adjustments and overcoming challenges posed by dynamic water environments.The PID controller dynamically adjusts the boat's heading based on real-time feedback, ensuring smoother path execution and faster convergence towards the optimal route.This innovative synergy between a classical pathfinding algorithm and a feedback control system addresses the complexities of water-based scenarios, where unpredictable currents, obstacles, and varying conditions necessitate adaptive strategies.The proposed PIDenhanced A* algorithm not only enhances path planning accuracy but also exhibits improved resilience in the face of environmental uncertainties, making it a promising solution for efficient and reliable autonomous watercraft navigation in diverse and challenging aquatic settings.the results show that the A* algorithm with PID controller is superior to the original A* without PID controller with respect to mean path length and standard deviation with a reduction of up to 23% which leads to improved path planning for proposed environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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