College of IOT Engineering, Hohai University, Changzhou 213022, China.
Jiangsu Universities and Colleges Key Laboratory of Special Robot Technology, Hohai University, Changzhou 213022, China.
Comput Intell Neurosci. 2018 Dec 2;2018:2186574. doi: 10.1155/2018/2186574. eCollection 2018.
Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.
多自主水下航行器（AUV）在未知三维水下环境中的协同目标搜索问题不仅是一个研究热点，也是一个具有挑战性的任务。为了完成这项任务，每艘自主水下航行器（AUV）都需要快速移动而不发生碰撞，并与其他 AUV 合作找到目标。在本文中，提出了一种基于改进海豚群算法（DSA）的方法，并将搜索问题分为三个阶段，即随机巡航、动态联盟和团队搜索。在提出的方法中，使用莱维飞行方法为 AUV 提供随机游走，以便在随机巡航阶段检测目标信息。然后使用自组织映射（SOM）神经网络实时建立动态联盟。最后，提出了一种改进的 DSA 算法来实现团队搜索。此外，进行了一些仿真，结果表明，所提出的方法能够有效地指导多 AUV 在未知三维水下环境中实现目标搜索任务。
