基于多蚁群协同搜索算法的多AUV路径规划

岳 伟; 席 云; 关显赫

doi:10.11993/j.issn.2096-3920.2020.05.005

基于多蚁群协同搜索算法的多AUV路径规划

doi: 10.11993/j.issn.2096-3920.2020.05.005

大连海事大学船舶电气工程学院, 辽宁大连, 116026

基金项目: 国家自然科学基金(61703072); 大连市科技创新基金(2019J12GX040); 智能感知与先进控制国家民委重点实验室开放基金(MD-IPAC-201901); 中央高校基本科研业务费(3132019355).

详细信息

作者简介:
岳伟(1981-), 男, 博士, 副教授, 主要研究方向为无人集群协同规划及控制.

中图分类号: U674.941 TP29
计量
- 文章访问数: 1027
- HTML全文浏览量: 55
- PDF下载量: 206
- 被引次数: 0
出版历程
- 收稿日期: 2020-02-12
- 修回日期: 2020-04-10
- 刊出日期: 2020-10-31

Path Planning of Multi-AUVs Based on Multi-ant Colony Cooperative Search Algorithm

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China

摘要

摘要: 针对未知环境下多自主水下航行器(AUVs)在不考虑声呐探测距离, 优化指标单一情况下的协同搜索问题, 综合探测距离对目标发现概率的影响, 以及AUV转向和避碰威胁等影响因素, 提出一种基于先验信息的多蚁群协同路径规划算法。首先, 根据目标分布的先验信息建立基于搜索区域栅格化的目标概率分布图; 然后, 按照目标概率分布初始化信息素浓度, 利用先验信息指导各种群蚂蚁搜索, 并根据目标概率大小设计状态转移规则, 使得目标发现概率最大化; 最后, 按照搜索路径解的优劣来更新信息素浓度。仿真验证了文中搜索策略的有效性。
- 自主水下航行器 /
- 蚁群算法 /
- 协同搜索 /
- 路径规划 /
- 信息素
Abstract: To solve the cooperative search problem of multiple autonomous undersea vehicles(AUVs) in an unknown en-vironment without considering the sonar detection distance and single optimization index, a collaborative path planning algorithm for multi-ant colonies based on prior information is proposed based on the comprehensive effect of detection distance on target discovery probability, AUV turning, and collision avoidance threat. First, based on the prior information of the target distribution, a target probability distribution map based on the grid of the search area is established. Subsequently, the pheromone concentration is initialized based on the probability distribution of the target, and the prior information is used to guide ant searches. State transition rules are designed based on the target probability size to maximize the probability of finding the target. Finally, the pheromone concentration is updated based on the advantages and disadvantages of the search path solution, and the effectiveness of the search strategy is verified by simulation.
- autonomous undersea vehicle /
- ant colony algorithm /
- cooperative search /
- path planning /
- pheromone

HTML全文

参考文献(12)

[1]	Wynn R B, Huvenne, Veerle A I, et al. Autonomous Un-derwater Vehicles(AUVs): Their Past, Present and Future Contributions to the Advancement of Marine Geoscience [J]. Marine Geology, 2014, 352(2): 451-468.
[2]	王童豪, 彭星光, 潘光, 等. 无人水下航行器的发展现状与关键技术[J]. 宇航总体技术, 2017, 1(4): 52-64. Wang Tong-hao, Peng Xing-guang, Pan Guang, et al. Development and Key Technologies of Unmanned Under-water Vehicles[J]. Astronautically Systems Engineering Technology, 2017, 1(4): 52-64.
[3]	钟宏伟. 国外无人水下航行器装备与技术现状及展望[J]. 水下无人系统学报, 2017, 25(4): 215-225. Zhong Hong-wei. Review and Prospect of Equipment and Techniques for Unmanned Undersea Vehicle in Foreign Countries[J]. Journal of Unmanned Undersea Systems, 2017, 25(4): 215-225.
[4]	Zhu D, Huang H, Yang S X. Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace[J]. IEEE Transactions on Cybernetics, 2013, 43(2): 504-514.
[5]	Zhu D, Yang Y, Yan M. Path Planning Algorithm for AUV Based on a Fuzzy-PSO in Dynamic Environments[C]// Eighth International Conference on Fuzzy Systems & Knowledge Discovery. Shanghai, China: IEEE, 2011.
[6]	Wang H J, Xiong W. Research on Global Path Planning based on Ant Colony Optimization for AUV[J]. Journal of Marine Science & Application, 2009, 8(1): 58-64.
[7]	朱大奇, 曹翔. 多个水下机器人动态任务分配和路径规划的信度自组织算法[J]. 控制理论与应用, 2015, 32(6): 762-769. Zhu Da-qi, Cao Xiang, An Improved Self-organizing Map Method for Multiple Autonomous Underwater Vehicles Teams in Dynamic Task Assignment and Path Planning[J]. Control Theory & Applications, 2015, 32(6): 762-769.
[8]	Wen Z W, Kun L M, Wang L J. Path Planning for UUV in Dynamic Environment[C]//2016 9th International Symposium on Computational Intelligence and De-sign(ISCID). Hangzhou, China: IEEE, 2016.
[9]	张跟鹏, 刘忠, 田晓东. 基于UUV组群的水下协同探雷搜索与仿真[J]. 自动控制与测量, 2007(4): 62-64, 67. Zhang Gen-Peng, Liu Zhong, Tian Xiao-dong. Searching and Simulation of Underwater Cooperative Mine Detection Based on UUV Group[J]. Automatic Measurement and Control, 2007(4): 62-64, 67.
[10]	Yoon S, Qiao C. Cooperative Search and Survey Using Autonomous Underwater Vehicles(AUVs)[J]. IEEE Transactions on Parallel and Distributed Systems, 2010, 22(3): 364-379.
[11]	Dorigo M, Stützle T. Ant Colony Optimization Theory [M]. Massachusetts, USA: MIT Press, 2004: 121-152.
[12]	Dorigo M, Blum C. Ant Colony Optimization Theory: A survey[J]. Theoretical Computer Science, 2005, 344(2-3): 243-278.