CoUAV: a multi-UAV cooperative search path planning simulation environment
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".