# AeroGen: Agentic Drone Autonomy through Single-Shot Structured Prompting & Drone SDK

arXiv: https://arxiv.org/abs/2603.14236 · HTML: https://mlsanity.com/paper/2603.14236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 15 of 15)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [L3MVN: Leveraging Large Language Models for Visual Target Navigation](https://mlsanity.com/paper/2304.05501/) (2023)
- [Autonomous Power Line Inspection with Drones via Perception-Aware MPC](https://mlsanity.com/paper/2304.00959/) (2023)
- [FogROS: An Adaptive Framework for Automating Fog Robotics Deployment](https://mlsanity.com/paper/2108.11355/) (2021)
- [GSCE: A Prompt Framework with Enhanced Reasoning for Reliable LLM-driven Drone Control](https://mlsanity.com/paper/2502.12531/) (2025)
- [Industry 6.0: New Generation of Industry driven by Generative AI and Swarm of Heterogeneous Robots](https://mlsanity.com/paper/2409.10106/) (2024)
- [Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems](https://mlsanity.com/paper/2303.18237/) (2023)
- [ImpedanceGPT: VLM-driven Impedance Control of Swarm of Mini-drones for Intelligent Navigation in Dynamic Environment](https://mlsanity.com/paper/2503.02723/) (2025)
- [Large Language Model-Driven Closed-Loop UAV Operation with Semantic Observations](https://mlsanity.com/paper/2507.01930/) (2025)
- [AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service](https://mlsanity.com/paper/2504.03802/) (2025)
- [LLM-Driven Corrective Robot Operation Code Generation with Static Text-Based Simulation](https://mlsanity.com/paper/2512.02002/) (2025)
- [Software Engineering for Self-Adaptive Robotics: A Research Agenda](https://mlsanity.com/paper/2505.19629/) (2025)
- [AeroDaaS: A Programmable Drones-as-a-Service Platform for Intelligent Aerial Systems](https://mlsanity.com/paper/2603.00506/) (2026)

## Abstract (arXiv)

Designing correct UAV autonomy programs is challenging due to joint navigation, sensing and analytics requirements. While LLMs can generate code, their reliability for safety-critical UAVs remains uncertain. This paper presents AeroGen, an open-loop framework that enables consistently correct single-shot AI-generated drone control programs through structured guardrail prompting and integration with the AeroDaaS drone SDK. AeroGen encodes API descriptions, flight constraints and operational world rules directly into the system context prompt, enabling generic LLMs to produce constraint-aware code from user prompts, with minimal example code. We evaluate AeroGen across a diverse benchmark of 20 navigation tasks and 5 drone missions on urban, farm and inspection environments, using both imperative and declarative user prompts. AeroGen generates about 40 lines of AeroDaaS Python code in about 20s per mission, in both real-world and simulations, showing that structured prompting with a well-defined SDK improves robustness, correctness and deployability of LLM-generated drone autonomy programs.