A NOVEL APPROACH TO PATH PLANNING FOR AUTONOMOUS MOBILE ROBOTS
Why this work is in the frame
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Bibliographic record
Abstract
Path planning is considered as one of the core problems of autonomous mobile robots. Different approaches have been proposed with different levels of complexity, accuracy, and applicability. This paper presents a hybrid approach to the problem of path planning that can be used to find global optimal collision-free paths. This approach relies on combining potential field (PF) method and genetic algorithm (GA) which takes the strengths of both and overcomes their inherent limitations. In this integrated frame, the PF method is designed as a gradient-based searching strategy to exploit local optimal, and the GA is used to explore over the whole problem space. In this work, different implementing strategies are examined in different complexity scenarios. The conducted experiments show that global optimal paths can be achieved effectively using the proposed approach with a strategy of high diversity and memorization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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