LLM-based UAV Path Planning for Autonomous and Adaptive Industry Systems

In the era of Industry 5.0, Unmanned Aerial Vehicles (UAVs) equipped with multiple sensors play a vital role in industrial tasks such as patrol and surveillance, offering distinct advantages of high mobility, accurate perception, and autonomous operation. However, traditional path planning methods for UAVs struggle with challenges related to interpretability and adaptation to dynamic industrial environments. To address these challenges, this paper proposes a novel LLM-based approach to UAV swarm path planning in autonomous and adaptive industrial systems. The proposed method aims to minimize the time and computational consumption of path planning while improving the task completion rate of UAVs. Specifically, we first propose the multi-step deep thinking movement decision samples generation algorithm (MSDTMD-SG) to generate deep thinking training samples for LLMs at different ends and fine-tune them. Second, we design a scene memory and replay learning mechanism, enabling damaged UAVs to store perceived information and generate training samples via MSDTMD-SG for continuous LLM learning and optimization. Finally, extensive experiments demonstrate that the proposed method exhibits strong adaptability to dynamic environments, achieves the highest task completion rate among all methods, and maintains competitive system consumption.