# GSCE: A Prompt Framework with Enhanced Reasoning for Reliable LLM-driven Drone Control

arXiv: https://arxiv.org/abs/2502.12531 · HTML: https://mlsanity.com/paper/2502.12531/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 1 of 1)

- [TypeFly: Flying Drones with Large Language Model](https://mlsanity.com/paper/2312.14950/) (2023)

## Built on by (most cited first, 7 of 7)

- [GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics](https://mlsanity.com/paper/2506.14009/) (2025)
- [Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones](https://mlsanity.com/paper/2605.03788/) (2026)
- [A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspection](https://mlsanity.com/paper/2510.00259/) (2025)
- [CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios](https://mlsanity.com/paper/2505.00091/) (2025)
- [End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent](https://mlsanity.com/paper/2607.06964/) (2026)
- [Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning](https://mlsanity.com/paper/2506.04998/) (2025)
- [AeroGen: Agentic Drone Autonomy through Single-Shot Structured Prompting & Drone SDK](https://mlsanity.com/paper/2603.14236/) (2026)

## Abstract (arXiv)

The integration of Large Language Models (LLMs) into robotic control, including drones, has the potential to revolutionize autonomous systems. Research studies have demonstrated that LLMs can be leveraged to support robotic operations. However, when facing tasks with complex reasoning, concerns and challenges are raised about the reliability of solutions produced by LLMs. In this paper, we propose a prompt framework with enhanced reasoning to enable reliable LLM-driven control for drones. Our framework consists of novel technical components designed using Guidelines, Skill APIs, Constraints, and Examples, namely GSCE. GSCE is featured by its reliable and constraint-compliant code generation. We performed thorough experiments using GSCE for the control of drones with a wide level of task complexities. Our experiment results demonstrate that GSCE can significantly improve task success rates and completeness compared to baseline approaches, highlighting its potential for reliable LLM-driven autonomous drone systems.