TPML: Task Planning for Multi-UAV System with Large Language Models
Jinqiang Cui, Guocai Liu, Hui Wang, Yue Yu, Jiankun Yang
Abstract
Efficient task planning is pivotal for multi-UAV systems navigating dynamic environments. Traditional task planning methods face challenges in adapting to the constantly changing scenarios. The emergence of large language models (LLMs) offers promising solutions to bridge this gap. Our proposal, TPML, leverages LLMs as a command interface to comprehend operators' intentions and translate them into executable codes. Harnessing the creative capabilities of generative models, TPML can command multiple UAVs in both synchronous and asynchronous patterns with a single natural-language input. Experimental results are initially validated in a tailored simulation environment before transitioning to practical implementations. Successful demonstrations of both synchronous and asynchronous missions in real-world scenarios underscore the efficacy of TPML.