jev-drone
Use Jev for tactical judgments in a camera-driven drone simulation.
What Jev decides
Which maneuver fits, how risky the scene is, and whether the target is lost.
jev-drone flies a MuJoCo obstacle course with camera-derived scene facts, Jev maneuver advice, and code-owned flight safety.
- Jev role
- Advise the drone on tactical maneuver and scene risk.
- Input
- Camera-derived range sectors, obstacle features, and target status.
- Output
- Choice, Score, and Noul judgments consumed by the guidance code.
- Decision frequency
- Around 2.5 Hz when the scene warrants a new judgment.
- Fallback
- Local guidance and safety reflexes retain control and can veto Jev advice.
How it works
Overview
Computer vision turns depth and segmentation images into a compact scene. Jev chooses a maneuver, scores risk, and judges whether the target is truly lost. A faster guidance and reflex layer controls flight, can veto unsafe proposals, and reuses judgments when the scene is unchanged. The README reports a full-course flight and a local baseline comparison; those are author-reported simulation results, not evidence of a physical drone test.
- Language
- Python
- License
- MIT
- Author
- RomanSlack
Sources
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