A PSO-based approach to cooperative foraging tasks of multi-robots in completely unknown environments
Why this work is in the frame
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Bibliographic record
Abstract
Cooperative foraging tasks in unknown environments are fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are desirable for multi-robot cooperation. In this paper, an improved potential field-based PSO (IPPSO) approach is applied to accomplish the cooperative foraging tasks in completely unknown environments, compared to the cases using the PPSO approach. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the added dynamic parameter tuning and district-difference degree can increase the work efficiency, and help the multi-robot system to complete the tasks in complex environments. In the simulation studies, various scenarios are investigated. The effectiveness of the proposed approach 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 | 0.000 |
| 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