FAIR^2 Drones is a proposed standard that adds platform metadata and annotation specifications to existing FAIR and AI-ready frameworks so wildlife drone datasets can support ecological analysis, robotics development, and computer vision benchmarking simultaneously.
GSCE: A prompt framework with enhanced reason- ing for reliable LLM-driven drone control
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Proposes a role-differentiated multi-agent trajectory planning framework for threat-aware autonomy in dynamic adversarial environments using CSBEZ guidance laws to improve team mission success via redundancy and threat saturation.
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
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FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
FAIR^2 Drones is a proposed standard that adds platform metadata and annotation specifications to existing FAIR and AI-ready frameworks so wildlife drone datasets can support ecological analysis, robotics development, and computer vision benchmarking simultaneously.