摘要
针对水下无人航行器(UUV)集群在有限续航力和负载约束条件下求解广泛且稀疏分布区域勘察任务规划问题时常规算法存在收敛性差、解质量不高的不足,提出了一种改进的蚁群优化算法。首先,通过分析个体UUV平台能力和集群任务的约束条件,建立UUV集群任务规划的约束模型和优化模型;其次,基于任务点间距离与平均距离之差设计初始信息素浓度的非均等分配方法,提出优化模型的最佳与最差阈值对蚂蚁进行分类并对应完成信息素更新,在状态转移规则中创新加入可随迭代进程动态改变的“引力系数”来增加算法前中期次优节点被选中的概率;再次,设置对照统计实验完成算法优化项的有效性分析,依据最优解出现的次数和平均收敛值优化算法参数;最后,以经典文献案例仿真,对比分析基本蚁群算法、精英蚁群算法与提出算法,相较于前两种算法,算法在50次统计实验中找到近似最优解的百分比分别提升78%和66%,平均在第40代实现收敛,表明出很好的全局寻优能力和收敛性能。通过设计具有一定规模的UUV集群任务规划典型案例,验证了算法求解UUV集群广泛且稀疏分布区域任务规划问题的快速性和有效性。
When conventional algorithms are used to solve the survey task planning problem of unmanned underwater vehicle(UUV)swarm under limited endurance and load constraints in a wide and sparsely distributed area,the poor convergence and low solution quality are common problems.In this article,an improved ant colony optimization algorithm is proposed.Firstly,by analyzing the constraints of individual UUV platform capability and swarm task,the constraint model and optimization model of UUV swarm task planning are formulated.Secondly,a method of unequal allocation of initial pheromone concentration is designed based on the difference between the average distance and the distance between task points,the optimal and worst thresholds of the optimization model are proposed to classify the ants and complete pheromone update,introduce an innovative“gravity factor”that dynamically changes according to the iteration process to the state transition rule to increase the probability of the suboptimal node being selected in the early and middle of the algorithm.Thirdly,the validity of the algorithm optimization item is analyzed by statistical experiment and the algorithm parameters are optimized according to the number of optimal solutions and average convergence value.Finally,based on the cases from classic documents,by making comparison analysis with basic ant colony algorithm and elite ant colony algorithm,the proposed algorithm in this article improves the percentage of finding approximate optimal solution by 78%and 66%in 50 statistical experiments and average convergence in the 40 th generation,which shows good global optimization capability and convergence performance.Experimental results of a designed typical UUV swarm mission planning case with a certain scale show the rapidity and effectiveness of this algorithm in solving the problem of the swarm survey task planning in a wide and sparsely distributed area.
作者
王宏健
鄂鑫
张凯
易冬波
牛帅
Wang Hongjian;E Xin;Zhang Kai;Yi Dongbo;Niu Shuai(Harbin Engineering University,Harbin 150001,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第9期238-254,共17页
Chinese Journal of Scientific Instrument
基金
GF科技创新特区项目(21-163-05-ZT-002-005-03)
水下机器人重点实验室基金(JCKYS2022SXJQR-09)
哈尔滨工程大学“高水平科研引导专项”(3072022QBZ0403)项目资助。
