Abstract
Large Language Models (LLMs) have demonstrated revolutionary potential in autonomous systems. Studies have demonstrated that LLMs can be leveraged to support various robotic operations including visual navigation, planning, and drone controls. However, when facing tasks with complex reasoning, concerns and challenges are raised about the reliability of solutions produced by LLMs. This thesis proposed a closed-loop reasoning approach to enhance the reliability of LLM-driven drone control. The proposed method introduces a structured prompt framework designed with Guidelines, Skills, Constraints, and Examples integrated with a closed-loop feedback mechanism that iteratively refines the generated code based on its execution outcomes. The framework is featured by iteratively refining LLM-generated solutions, thereby improving reasoning quality and control reliability. We performed thorough experiments of our approach for drone control with a wide range of task complexities. The experiment result demonstrates that our framework significantly improves task success rates and completeness compared to baseline approaches, highlighting its potential for reliable LLM-driven autonomous drone systems.