End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
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-w...
| Authors | Amin Tabrizian, Arsyi Aziz, Aarifah Ullah et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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