Unmanned Vehicle Formation Control Based on Large Language Model

The advent of autonomous systems has revolutionized the field of unmanned vehicles (UVs), enabling complex operations without direct human intervention. A critical aspect of such systems is the ability to maintain formation in a dynamic environment, which is essential for tasks ranging from aerial surveillance to synchronized ground transportation. This paper presents a novel approach to UV formation control by leveraging the capabilities of Large Language Models (LLMs). We propose a framework (UVF-LM) where the LLM serves as a central decision-making entity and achieves formation control of multiple font characters through the skeleton positioning of external font libraries and coordinate calculation tools. The proposed method integrates the LLM’s advanced pattern recognition and predictive modeling capabilities with traditional control algorithms to facilitate real-time adjustments and cooperative behaviors among vehicles. The LLM’s understanding of context and ability to handle uncertainty allows the formation control system for more adaptivity and robustness in applications. The effectiveness of the proposed approach is demonstrated through a series of simulations. Compared with traditional methods, the proposed approach is endowed with a feature of improvement on the integrity, diversity, and adaptability of UV formation to environmental changes. The results indicate that incorporating LLM into unmanned vehicle systems can provide scalable solutions for complex formation control tasks.