用模糊安全约束和图检索增强大模型，让无人机在未知危险中更安全、更通用地决策。
SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision
- 用模糊控制屏障函数验证大模型输出，实现安全可证明
- 通过分层图结构检索，快速适应复杂场景变化
- 无需在线训练，在对抗场景中表现更稳定可靠
在无人机动态决策中，复杂的多变危险因素严重挑战算法的泛化能力。尽管大语言模型（LLM）具备语义理解与场景泛化能力，但缺乏领域特定的无人机控制知识和形式化安全保证，限制了其直接应用。本文提出一种无需训练的两层决策架构，融合高层安全规划与底层精确控制。框架包含三项关键贡献：1）基于语义增强动作的模糊控制屏障函数验证机制，为LLM输出提供可证明的安全认证；2）基于星型分层图的检索增强生成系统，实现高效、弹性且可解释的场景自适应；3）在未知障碍物与突发威胁的追逃场景中进行系统性实验验证，结果表明SAGE-LLM在不进行在线训练的前提下，显著提升安全性与泛化能力并保持性能。该框架展现出强可扩展性，有望推广至更广泛的具身智能系统与安全关键控制领域。
原文摘要 · Abstract (English)
In UAV dynamic decision, complex and variable hazardous factors pose severe challenges to the generalization capability of algorithms. Despite offering semantic understanding and scene generalization, Large Language Models (LLM) lack domain-specific UAV control knowledge and formal safety assurances, restricting their direct applicability. To bridge this gap, this paper proposes a train-free two-layer decision architecture based on LLMs, integrating high-level safety planning with low-level precise control. The framework introduces three key contributions: 1) A fuzzy Control Barrier Function verification mechanism for semantically-augmented actions, providing provable safety certification for LLM outputs. 2) A star-hierarchical graph-based retrieval-augmented generation system, enabling efficient, elastic, and interpretable scene adaptation. 3) Systematic experimental validation in pursuit-evasion scenarios with unknown obstacles and emergent threats, demonstrating that our SAGE-LLM maintains performance while significantly enhancing safety and generalization without online training. The proposed framework demonstrates strong extensibility, suggesting its potential for generalization to broader embodied intelligence systems and safety-critical control domains.
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