Harmonic Path Planning using Quarter-Sweep Boosted TOR Iterative Method
Bibliographic record
Abstract
This paper presents the study to examine the effectiveness of the application of Quarter Sweep Boosted TOR with the 9-Point Laplacian operator using the families of relaxation methods in the computation of Laplace equation solutions to obtain the harmonic potentials. This work is a continuation from the past study that applied the standard application 5-Point Laplacian to solve path planning issue which a mobile robot faces because of working in indoor environment. The robot can navigate from a given initial position to a goal position by following the safest path, ensuring it avoids any obstacles and minimizes the risk of collisions. By utilizing the equation of Laplace and computing the potential values’ distribution in the environments which have been simulated, the robot can determine the safest path that avoids obstacles which exists in the environment. This method ensures that the robot moves along a path where the potential for collisions is minimized. The findings confirm that QSBTOR outperforms Half Sweep Boosted TOR (HSBTOR) and Full Sweep Boosted TOR (FSBTOR). QSBTOR and HSBTOR show 73% and 50% reduction respectively, compared to FSBTOR in terms of computational complexity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".