自动化学报2025，Vol.51Issue(10)：2211-2231,21.DOI:10.16383/j.aas.c250223
集群协同任务规划的形式逻辑方法:综述与展望
Formal Logic-based Cooperative Task Planning for Multi-robot Systems:Survey of Recent Advances and Future Directions
摘要
Abstract
Unmanned swarm systems,composed of ground vehicles,aerial vehicles,and other platforms,have been widely applied in both military and civilian domains.As the decision-making core of the swarm,task planning faces multiple challenges,including temporal conflict coordination,large-scale heterogeneous collaboration,and adapta-tion to dynamic environments.Traditional mixed-integer optimization methods show clear shortcomings in express-ive flexibility and real-time solvability,while planning methods based on machine learning are inherently limited in interpretability and scalability.In recent years,formal logic methods,represented by linear temporal logic and sig-nal temporal logic,have emerged as critical tools for swarm task modeling and planning,owing to their advantages of precise task specification,rigorous logical reasoning,and strong interpretability.This paper systematically re-views research progress on swarm task planning based on formal logic,providing a comprehensive analysis of funda-mental syntax and semantics,planning paradigms,and adaptation mechanisms under large-scale and dynamically uncertain environments.It also explores the potential of large language models in natural language task understand-ing,formalized task modeling and task planning.Finally,future research directions are discussed,including continu-ous planning under incomplete environments,integrated planning of swarm tasks and motions,and closed-loop planning that combines formal logic with large language models.关键词
多机器人系统/自主无人系统/集群协同/任务规划/线性时序逻辑/信号时序逻辑/大语言模型Key words
Multi-robot systems/autonomous unmanned systems/swarm cooperation/task planning/linear tempor-al logic/signal temporal logic/large language models引用本文复制引用
李忠奎,王俊杰,张云奕,张硕,国萌,孙志勇..集群协同任务规划的形式逻辑方法:综述与展望[J].自动化学报,2025,51(10):2211-2231,21.基金项目
国家自然科学基金(U2241214,T2121002,62373008)资助Supported by National Natural Science Foundation of China(U2241214,T2121002,62373008) （U2241214,T2121002,62373008）
