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Drones, Volume 9, Issue 11
November 2025 - 83 articles
Cover Story: Ensuring the safety of high-altitude solar drones is paramount, but traditional manual analysis is slow and struggles with the complexity of flight control–energy coupling. While Large Language Models (LLMs) offer automation, generic reasoning is unreliable for such specialized, safety-critical tasks. K-EGoT is a framework that aligns an LLM’s reasoning with a verifiable, expert knowledge base. We introduce a “Safety Rationale”, an auditable link between the AI’s logic and curated safety principles. We then use a novel “thought process alignment” strategy to train the model on the quality of its reasoning, not just the final output. On a high-fidelity drone dataset, our K-EGoT model outperformed baselines, delivering a reliable and auditable solution for automated safety modeling. View this paper
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Drones - ISSN 2504-446X