摘要
多智能体协同路径规划(cooperative multi-agent path finding,Co-MAPF)在无人机编队、多智能体系统等领域得到广泛应用,通过多智能体之间的任务协作、路径规划与任务执行,以提升整体系统效率。从Co-MAPF问题的定义出发,介绍了集中式、分布式和混合式3种主要系统架构及其优缺点;对主流Co-MAPF算法进行了归类与评述,涵盖了基于采样、搜索、智能优化和学习的各类方法;在总结现有研究的基础上,分析了Co-MAPF算法当前面临的主要挑战,并展望了未来的发展方向。
Cooperative multi-agent path finding(Co-MAPF)has been widely applied in fields such as UAV formation and multi-agent systems,which enhances the overall system efficiency through task collaboration,path planning,and task execution among multiple agents.This paper introduced three main system architectures,namely centralized,distributed,and hybrid,along with their advantages and disadvantages based on the definition of the Co-MAPF problem,categorized,and reviewed mainstream Co-MAPF algorithms,including those based on sampling,search,intelligent optimization,and learning.Furthermore,this paper analyzed the main current challenges faced by Co-MAPF algorithms on the basis of summarizing existing research and outlined the future development directions.
作者
熊骏
张文博
熊智
周峰
杨博
Xiong Jun;Zhang Wenbo;Xiong Zhi;Zhou Feng;Yang Bo(School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《系统仿真学报》
北大核心
2025年第12期3033-3049,共17页
Journal of System Simulation
基金
国家自然科学基金(62203228,61873125)
航空科学基金(ASFC-2022Z0220X9001)
江苏省研究生科研与实践创新计划(KYCX25_1229)。
关键词
多智能体
协同路径规划
任务分配
协同控制
multi-agent
cooperative path finding
task assignment
collaborative control
