LLM-Based Dynamic Event-Triggered Communication for Multi-UAV Formation Control in Urban Environments
Jian Gu, Yin Wang, Wen Ji, Zhongxiang Wei, Jingjing Wang
Abstract
As a typical application of the low-altitude economy, UAV collaborative monitoring contributes to urban management and data collection. The dense distribution of urban buildings leads to limited perception and communication constraints, making it challenging for multi-UAV systems to achieve effective unified situational awareness. Therefore, this paper constructs a dynamic event-triggered communication strategy based on a large language model (LLM) and achieves collaborative control of multiple UAVs through deep reinforcement learning. Firstly, a LLM is used to analyze the environment code to actively understand the state of the UAV and the characteristics of the environment, effectively improving adaptability to complex environments. Then, LLM uses Python code to generate communication trigger conditions through semantic reasoning and makes dynamic adjustments to optimize communication timing and reduce network congestion and resource consumption. In addition, this paper proposes a coder-evaluator framework to solve the problems of high cost, low efficiency and intense subjectivity of the reinforcement learning human feedback (RLHF) method in LLM. In this way, the executable code generated by LLM is optimized to ensure the robustness and efficiency of the communication mechanism. Experiments in the high-fidelity AirSim simulation environment demonstrate significant improvements compared to baseline methods: a 26.58% decrease in communication cost, a 4.92% increase in task success rate, and a 21.43% acceleration in convergence speed.