用大模型+记忆检索实现人机协同的智能飞行规划
End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
- 结合大模型、多模态教练与检索增强记忆生成飞行计划
- 在多种场景下最高达93.8%的计划有效性，易场景下接近99%
- 适合关注人机协同与智能航空系统的研究人员
弥合人类飞行员意图与自主飞行操作之间的差距，对电动垂直起降（eVTOL）飞机的实际部署至关重要。传统飞行规划依赖经典算法，难以融入灵活的人类偏好。我们提出FRAMe，一个端到端的大语言模型（LLM）飞行规划系统，融合基于检索增强生成（RAG）的记忆模块与多模态教练代理。该系统将规划型大模型与多模态教练代理及RAG记忆相结合，生成既满足任务约束又符合飞行操作员偏好的飞行计划。我们在一系列不同难度的真实世界模拟场景中验证了该系统。在四种大模型上，完整版FRAMe系统（含RAG和教练）在所有规划器中均达到最高有效性（综合最高达93.8%，强规划器在简单场景下达99%），并在具备提升空间的偏好相关指标上向操作员偏好的方向转移。FRAMe展示了先进大模型如何用于以人为本的任务规划，将自然语言指令转化为安全、高效且灵活的飞行路径。代码已开源：github.com/amin-tabrizian/FlightPlanningLLMs
原文摘要 · Abstract (English)
Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment. Flight planning traditionally relies on classic algorithms that struggle to incorporate flexible human preferences. We present FRAMe, an End-to-End Large Language Model (LLM) Flight Planning tool with RAG-based Memory and Multi-modal Coach Agent. Our system integrates a planner LLM with a multi-modal coach agent and retrieval augmented generation (RAG)-based memory to generate flight plans that satisfy mission constraints while aligning with human flight operator preferences. We demonstrate the system in a range of real-world-inspired scenarios of varying difficulty levels. Across four LLMs, the full FRAMe system (RAG and coach) yields the highest validity for every planner (up to 93.8% aggregate, 99% on Easy scenarios for the strongest planner) and shifts preference-relevant metrics in the operator-favored direction where the metric has headroom. FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural language instructions into safe, efficient, and flexible flight routes. The code is available at: github.com/amin-tabrizian/FlightPlanningLLMs
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