计算机科学
任务(项目管理)
人工智能
人机交互
系统工程
工程类
作者
Jinqiang Cui,Guocai Liu,Hui Wang,Yue Yu,Jiankun Yang
标识
DOI:10.1109/icca62789.2024.10591846
摘要
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.
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