摘要
为有效规划针对运动目标的多无人机最优全局搜索路径,提出一种基于协同演化运动编码粒子群优化(CC-MPSO)的方法.首先基于马尔可夫过程构建目标运动概率模型,将目标搜索问题转化为路径优化问题;然后设计CC-MPSO方法求解该问题,使目标被发现的累积概率最大化,得到最优搜索路径节点.该方法的运动编码机制将搜索路径节点转化为一组运动轨迹参数,使无人机的轨迹处理更加灵活;基于协同演化框架使不同无人机之间的协作能够通过多种群信息共享来提高全局搜索性能.仿真结果表明,相比五种主流群智能方法,本文方法在收敛速度、路径质量及鲁棒性等指标上均表现出显著优势.
In order to effectively plan the optimal global search path of multiple UAVs for moving targets,a method based on co-evolutionary motion-encoded particle swarm optimization(CC-MPSO)is proposed.First,a target motion probability model is constructed based on the Markov process,and the target search is transformed into a path optimization issue.Then,the CC-MPSO method is designed to solve the problem,maximizing the cu-mulative probability of target discovery and obtaining the optimal search path nodes.The motion encoding mecha-nism of this method converts the search path nodes into a set of motion trajectory parameters,making the trajecto-ry processing of UAVs more flexible.Based on the co-evolution framework,the collaboration among different UAVs can improve the global search performance through multi-population information sharing.Simulation re-sults show that,compared with the five mainstream swarm intelligence methods,the proposed method shows sig-nificant advantages in terms of convergence speed,path quality and robustness.
作者
丁川
陈维义
程晗
DING Chuan;CHEN Weiyi;CHENG Han(Naval University of Engineering,Wuhan 430030,China;Weihai Detachment of the People’sArmed Police Force,Weihai 264200,China)
出处
《空天预警研究学报》
2025年第4期267-273,共7页
JOURNAL OF AIR & SPACE EARLY WARNING RESEARCH
关键词
多无人机协同
运动目标搜索
运动编码机制
协同演化粒子群优化
无人机路径规划
multi-UAV collaboration
moving target search
motion encoding mechanism
co-evolutionary particle swarm optimization
UAV path planning
