Co-evolutionary search path planning under constrained information-sharing for a cooperative unmanned aerial vehicle team
Why this work is in the frame
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Bibliographic record
Abstract
Mobile cooperative sensor networks are increasingly used for surveillance and reconnaissance tasks to support domain picture compilation. However, efficient distributed information gathering such as target search by a team of autonomous unmanned aerial vehicles (UAVs) remains very challenging in constrained environment. In this paper, we propose a new approach to learn resource-bounded multi-agent coordination for a multi-UAV target search problem subject to stringent communication bandwidth constraints in a dynamic uncertain environment. It relies on a new information-theoretic co-evolutionary algorithm to solve cooperative search path planning over receding horizons, providing agents with mutually adaptive and self-organizing behavior. The anytime coordination algorithm is coupled to a divergence-based information-sharing policy to exchange high-value world-state information under limited communication bandwidth. Computational results show the value of the proposed approach in comparison to a well-known reported technique.
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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 | 0.002 |
| 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