Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (1): 72-79.doi: 10.11896/jsjkx.241000038

• Technology Research and Application of Large Language Model • Previous Articles     Next Articles

SWARM-LLM:An Unmanned Swarm Task Planning System Based on Large Language Models

LI Tingting1, WANG Qi1,2, WANG Jiakang1,2, XU Yongjun1,2   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2 University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2024-10-09 Revised:2024-11-26 Online:2025-01-15 Published:2025-01-09
  • About author:LI Tingting,born in 1997,master.Her main research interests include group decision-making intelligence and large model theory and application.
    WANG Qi,born in 1985,Ph.D,assiciate professor,Ph.D supervisor,is a sensior member of CCF(No.77141M).Her main research interests include intelligent wireless networks and LLM assisted decision making.

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Abstract: In response to the issues of insufficient autonomous intelligence in unmanned cluster systems,low collaborative efficiency 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 autonomous planning,efficient collaboration,and intelligent decision-making.This framework leverages large language models to transform 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 paper 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 unmanned 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%.

Key words: Task planning, Unmanned swarms, Large language models, Collaborative strategies, Intelligent decision-making

CLC Number: 

  • TP181
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