Multi-robot charged system search-based optimal path planning in static environments
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an optimal path planning approach based on Charged System Search (CSS) algorithms. The approach is applied to multiple mobile robots on holonomic wheeled platforms. Optimization problems (o.p.s) are defined for each robot to minimize the weighted sum of four objective functions whose minimization targets four path planning objectives. The CSS algorithms are mapped onto the o.p.s considering that the fitness functions are the objective functions, the search spare is the solution space, the agents (charged particles) are the mobile robots, and the population of agents is the set of mobile robots. Therefore, the optimal solutions to the o.p.s are the optimal paths. The new path planning approach is validated by experiments, and a comparison with other nature-inspired optimization-based path planning approaches is given.
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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