PID Controller Enhanced A* Algorithm for Efficient Water Boat
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".