改进头脑风暴算法在多AUV协同搜索动态目标中的应用
2024,46(6):203-209
高永琪
海军工程大学 兵器工程学院, 湖北 武汉 430033，gaoyq98@sina.cn,yichen_wp@163.com
王鹏
海军工程大学 兵器工程学院, 湖北 武汉 430033，gaoyq98@sina.cn,yichen_wp@163.com
马威强
中国人民解放军91959 部队, 海南 三亚 572000
海军工程大学 兵器工程学院, 湖北 武汉 430033，gaoyq98@sina.cn,yichen_wp@163.com
王鹏
海军工程大学 兵器工程学院, 湖北 武汉 430033，gaoyq98@sina.cn,yichen_wp@163.com
马威强
中国人民解放军91959 部队, 海南 三亚 572000
摘要：
针对搜索水中动态目标问题,提出一种基于改进头脑风暴优化(brain storm optimization, BSO)算法的多自主式水下航行器(autonomous underwater vehicle, AUV)协同搜索方法。该方法采用基于马尔可夫过程的运动预测目标存在概率,联合探测信息与预测信息更新目标存在概率。AUV间共享目标存在概率、环境不确定度、协调信息素等信息,各自利用滚动优化策略规划搜索路径。对该方法进行了有效性和鲁棒性的仿真验证。仿真结果表明,该方法能搜索到不同运动模式的水中动态目标,搜索效果优于随机算法、遍历算法等传统算法和BSO智能算法,且对AUV的不同初始出发位置不敏感,提高了战术使用的灵活性。
针对搜索水中动态目标问题,提出一种基于改进头脑风暴优化(brain storm optimization, BSO)算法的多自主式水下航行器(autonomous underwater vehicle, AUV)协同搜索方法。该方法采用基于马尔可夫过程的运动预测目标存在概率,联合探测信息与预测信息更新目标存在概率。AUV间共享目标存在概率、环境不确定度、协调信息素等信息,各自利用滚动优化策略规划搜索路径。对该方法进行了有效性和鲁棒性的仿真验证。仿真结果表明,该方法能搜索到不同运动模式的水中动态目标,搜索效果优于随机算法、遍历算法等传统算法和BSO智能算法,且对AUV的不同初始出发位置不敏感,提高了战术使用的灵活性。
基金项目：
国家部委基金资助项目(3020605010201)
国家部委基金资助项目(3020605010201)
Application of improved brain storm optimization in multi-AUVs cooperative search moving targets
GAO Yongqi
College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China,gaoyq98@sina.cn,yichen_wp@163.com
WANG Peng
College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China,gaoyq98@sina.cn,yichen_wp@163.com
MA Weiqiang
The PLA Unit 91959, Sanya 572000, China
College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China,gaoyq98@sina.cn,yichen_wp@163.com
WANG Peng
College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China,gaoyq98@sina.cn,yichen_wp@163.com
MA Weiqiang
The PLA Unit 91959, Sanya 572000, China
Abstract：
A cooperative search method of multiple AUV (autonomous underwater vehicle) on the basis of improved BSO (brain storm optimization) algorithm was proposed to search underwater moving targets. The target motion was predicted on the basis of Markov process, both the detection information and prediction information were used to update the target existence probability. AUVs shared the target existence probability, environmental uncertainty, and the coordination of pheromones, then planed the search path by rolling optimization strategy. The effectiveness and robustness of the proposed method were verified by simulation. The simulation results show that the method can search moving targets under different motion patterns, the search effect is better than the random algorithm, traversal algorithm and BSO algorithm, it is not sensitive to different initial departure positions of AUVs, improving the flexibility of tactical use.
A cooperative search method of multiple AUV (autonomous underwater vehicle) on the basis of improved BSO (brain storm optimization) algorithm was proposed to search underwater moving targets. The target motion was predicted on the basis of Markov process, both the detection information and prediction information were used to update the target existence probability. AUVs shared the target existence probability, environmental uncertainty, and the coordination of pheromones, then planed the search path by rolling optimization strategy. The effectiveness and robustness of the proposed method were verified by simulation. The simulation results show that the method can search moving targets under different motion patterns, the search effect is better than the random algorithm, traversal algorithm and BSO algorithm, it is not sensitive to different initial departure positions of AUVs, improving the flexibility of tactical use.
收稿日期：
2022-08-29
2022-08-29
