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This paper introduces FRAMe, an End-to-End Large Language Model (LLM) Flight Planning tool that integrates a planner LLM with a multi-modal coach agent and retrieval augmented generation (RAG)-based memory to create flight plans that align with human operator preferences. The system was evaluated across various real-world-inspired scenarios, achieving an aggregate validity of 93.8% and demonstrating significant improvements in operator-favored metrics. FRAMe highlights the potential of advanced LLMs to enhance human-centric mission planning in eVTOL aircraft operations by effectively translating natural language instructions into optimal flight routes.
FRAMe achieves up to 99% validity in easy scenarios, showcasing how LLMs can seamlessly align autonomous flight planning with human preferences.
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
