空天预警研究学报2025，Vol.39Issue(4)：267-273,7.DOI:10.3969/j.issn.2097-180X.2025.04.008
协同演化运动编码粒子群优化的多无人机搜索路径规划
Multi-UAV search path planning based on co-evolutionary motion-encoded particle swarm optimization
丁川 1陈维义 2程晗2
作者信息
- 1. 海军工程大学,武汉 430033||武警威海支队,山东 威海 264200
- 2. 海军工程大学,武汉 430033
- 折叠
摘要
Abstract
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.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Key words
multi-UAV collaboration/moving target search/motion encoding mechanism/co-evolutionary particle swarm optimization/UAV path planning分类
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丁川,陈维义,程晗..协同演化运动编码粒子群优化的多无人机搜索路径规划[J].空天预警研究学报,2025,39(4):267-273,7.
