基于遗传算法的UUV应召搜潜路径规划  

Improved Genetic Algorithm Based Path Planning of UUVs in On-Call Submarine Search

作　　者：付留芳 周明[1] 李文哲[1] 董晓明[1] FU Liufang;ZHOU Ming;LI Wenzhe;DONG Xiaoming(Department of Underwater Weaponry&Chemical Defense,Dalian Naval Academy,Dalian 116000,China)

机构地区：[1]海军大连舰艇学院水武与防化系,辽宁大连116000

出　　处：《电光与控制》2024年第7期42-47,86,共7页Electronics Optics & Control

摘　　要：针对应召搜潜中的两种常见情况,即目标随机机动和已知目标概略航向,建立了目标位置分布模型。针对无人航行器(UUV)对潜应召搜索中目标运动状态难以准确描述的问题,基于隐马尔可夫模型(HMM)建立了目标运动模型,能够在已知目标起始概率分布、转移概率和探测结果时实时更新目标概率分布。针对传统搜索方法难以保证有限搜索时间内发现概率最大的问题,设计了一种基于改进遗传算法的UUV应召搜潜路径规划方法,在常规遗传算法基础上增加删除和增添等操作,保证搜索路径的连通性;设计保留精英基因操作,使算法快速收敛。通过与扩方搜索、巡逻线搜索、随机搜索等常用搜索方法的对比,验证了该方法的有效性。Target location distribution model is established to describe two common situations of the submarine in on-call search including random motion and known approximate course.Aiming at the problem that it is difficult for the Unmanned Underwater Vehicles(UUVs)to describe accurately the target motion status in on-call submarine searching a target motion model is established based on hidden Markov Model(HMM).It can then update the probability distribution of the target in real time when the target initial probability distribution transition probability and the detection result are known.The other problem in submarine searching is that the traditional submarine searching methods may not get the largest detection probability in limited time.A submarine searching path planning method based on improved Genetic Algorithm(GA)is designed for the UUV which adds delete and insert operations on the basis of the traditional GA operations to guarantee connectivity of the search path.What s more the elite reservation operation is designed to ensure the fast convergence of the algorithm.The effectiveness of the proposed method is verified through the comparison with such common searching methods as extended position searching patrol line searching and random searching in simulation experiments.

关 键 词：UUV 应召搜索 路径规划 遗传算法 隐马尔可夫模型 

分 类 号：E925.6[军事—军事装备学]