Adaptive Charged System Search Approach to Path Planning for Multiple Mobile Robots
Why this work is in the frame
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Bibliographic record
Abstract
This paper suggests the application of adaptive Charged System Search (CSS) algorithms to the optimal path planning (PP) of multiple mobile robots. An off-line adaptive CSS-based PP approach is proposed and applied to holonomic wheeled platforms in static environments. The adaptive CSS algorithms solve the optimisation problems that aim the minimisation of objective functions (o.f.s) specific to PP and expressed as the weighted sum of four functions that target separate PP objectives. A penalty term is added in certain situations in the first step of the PP approach. The specific features of the adaptive CSS algorithms are the adaptation of the acceleration, velocity, and separation distance parameters to the iteration index, and the substitution of the worst charged particles’ fitness function values and positions with the best performing particle data. The fitness function in the adaptive CSS algorithms corresponds to the o.f., and the search space and agents (charged particles) in the adaptive CSS algorithms correspond to the solution space and to the mobile robots, respectively. A case study and experiments are included validate the new adaptive CSS-based PP approach and to compare it with non- adaptive CSS-, Particle Swarm Optimization- and Gravitational Search Algorithm-based PP approaches.
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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 | 0.000 | 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