Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot
Bibliographic record
Abstract
Unmanned aerial vehicles have advanced quickly and are now a dominant force in several domains, including military, security, and even logistics. 3D simulations with virtual environments are cost effective tools for assessing feasibility and costs of developing new systems or upgrading current systems. This is especially true for multiagent or swarm systems that require many agents working in concert. The total costs can be much higher when only physical prototypes are used for testing and analysis. There are existing 3D libraries for aiding with UAV simulations, but they’re too specialized; it is difficult to create highly customized virtual environments with these packages. A simulation development framework needs to be capable and stable for accurate simulations yet generalized enough that any types of simulations can be developed without exhausting efforts. This paper explores the feasibility and ease of using Godot, an open-source game engine, to develop a simulation that can assess the function of a multiagent UAV system. It is found that the Godot engine is a powerful tool that is both accurate and generalized enough to develop highly customizable UAV simulation. This allows verification and testing of UAV designs and control algorithms in highly customizable and flexible virtual environments, using a free open-sourced tool.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".