摘要
针对复杂障碍物环境下获取可供选择的多种无人机路径规划方案难的问题,探讨多目标无人机路径规划模型的构建及其求解算法。模型设计方面,以无人机的飞行距离和威胁代价为性能指标,同时以飞行空间范围、转向角和爬升角为约束,建立多目标无人机路径规划模型。随后,针对多目标樽海鞘算法(MSSA)存在初始种群分布均匀性差、无法有效平衡全局搜索和局部勘探等问题,借助Hammersley序列增强初始种群分布的均匀性,同时建立新型领导者-跟随者更新策略以及自适应混合策略增强种群寻优能力,获得改进型MSSA(IMSSA)。实验表明,该算法的求解性能具有明显优势,且能有效地获取无人机的多种可选路径规划方案。
To solve the problems that it is diffcult to obtain multiple alternative unmanned aerial vehicle(UAV)path planning schemes in complex obstacle environments,the construction of a multi-objective UAV path planning model and its solving algorithm are explored.In terms of model design,the flight distance and threat cost of the UAV are taken as the performance indicators,and the flight space range,steering angle,and climb angle are regarded as constraints.Subsequently,to address the problems of poor uniformity of initial population distribution and failure to effectively balance global search and local exploitation in the multi-objective salp swarm algorithm(MSSA),the Hammersley sequence is used to enhance the uniformity of the initial population distribution.At the same time,a new leader-follower update strategy and an adaptive hybrid strategy are established to enhance the population optimization capability,thus obtaining an improved MSSA(IMSSA).Experiments show that the proposed algorithm has obvious advantages in solution performance and can effectively obtain multiple alternative path planning schemes for UAVs.
作者
樊泷格
张著洪
FAN Longge;ZHANG Zhuhong(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)
出处
《传感器与微系统》
北大核心
2026年第4期130-135,共6页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(62063002)。
