SWARM-LLM:An Unmanned Swarm Task Planning System Based on Large Language Models
In response to the issues of insufficient autonomous intelligence in unmanned cluster systems,low collaborative effi-ciency of heterogeneous unmanned clusters,and unbalanced task allocation,this paper first proposes a new unmanned cluster task planning framework(SWARM-LLM)based on large language models to meet the needs of unmanned swarm systems for autono-mous planning,efficient collaboration,and intelligent decision-making.This framework leverages large language models to trans-form high-level task instructions into specific intelligent unmanned cluster task planning solutions,achieving collaborative tasks of unmanned clusters through multiple stages such as task decomposition,task allocation,and task execution.Furthermore,this pa-per designs a prompt engineering method specifically suited for unmanned swarm planning,called the planning chain(PC),to guide and optimize the implementation of these stages.Finally,we construct tasks of various categories and complexities in an un-manned swarm simulation environment(AirSim)and conduct evaluation experiments.Compared with other algorithms based on optimization and machine learning,experimental results demonstrate the effectiveness of the SWARM-LLM framework,showing a significant advantage in task success rates,with an average performance improvement of 47.8％.
Task planningUnmanned swarmsLarge language modelsCollaborative strategiesIntelligent decision-making
万方数据
维普
