CoUAV: a multi-UAV cooperative search path planning simulation environment
Why this work is in the frame
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Bibliographic record
Abstract
Sophisticated multi-unmanned aerial vehicle (UAV) simulation environments developed so far intrinsically paid significant attention to high-fidelity flight control system components to realistically account for low-level decision support. However, the use of these simulators often incurs a large overhead when focusing on cooperative high-level decision tasks, such as planning in mobile sensor networks. Therefore, a new discrete-event simulation environment, specially designed to investigate multi-agent search path planning coordination problems for surveillance and reconnaissance is proposed. Named CoUAV, the simulation capability gives the flexibility to define and customize simulation configurations from high-level abstract key components and stochastic events specifically aimed at exploring team coordination strategies for distributed information gathering. It abstracts away costly low-level system specifications. The environment provides the user with problem definition, visualization and post-simulation solution analysis capabilities. The versatility and flexibility of the environment is well-suited to explore the strengths and weaknesses of new and existing coordination strategies through comparative performance analysis over a variety of resource-bounded search path planning problem conditions. As an example, simulation results are presented for a military multi-UAV reconnaissance/target search mission comparing two solution designs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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