Online Optimization Application on Path Planning in Unknown Environments
Why this work is in the frame
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Bibliographic record
Abstract
For autonomous mobile robots, determining the shortest path to the target is an indispensable requirement. In this work, two modifications of the Grey Wolf Optimization (GWO) method, which are called MGWO1 and MGWO2, are suggested for online path planning to make the mobile robot reach the goal using the shortest path and safely avoiding the obstacles in unknown environments. To avoid sharp curves, a cost function is derived using a path smoothing parameter and an integrated distance function. The results of the proposed approach are presented based on computer simulation in various unknown environments. A study was conducted to compare the performance of the proposed algorithm with those of other algorithms and the results indicated that the proposed GWO, MGWO1, and MGWO2 algorithms are competent in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rate in path length compared with Adaptive Particle Swarm Optimization (APSO), GWO is 5.30%, MGWO1 is 5.52%, and MGWO2 is 7.44%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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