An improved PSO-based approach with dynamic parameter tuning for cooperative target searching of multi-robots
Why this work is in the frame
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Bibliographic record
Abstract
Multi-robot cooperation for target searching in completely unknown environments is a challenging topic that receives increasing attentions. In this paper, a novel potential field-based particle swarm optimization (PPSO) approach is applied for a team of mobile robots to cooperatively search for and reach targets in completely unknown environments. The target locations are unknown, where the robots explore the area and find the targets in a reasonable and effective way. The potential field function is the fitness function of the PSO, which is used to evaluate the exploration priority of the unknown area. The cooperation rules are defined in the proposed approach to lead the multi-robot system to explore the unknown environment. In addition, the district-difference degree and dynamic parameter tuning is added in the improved PPSO approach (IPPSO) to help the multi-robot system to complete complex tasks. The parameter setting is discussed in the simulation studies, and the effects of the parameter tuning is demonstrated by the experiment results.
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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 | 0.001 |
| 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