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CLC number: TP249

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2019-03-14

Cited: 0

Clicked: 6594

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Da-qi Zhu

http://orcid.org/0000-0001-7252-4952

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.3 P.330-341

http://doi.org/10.1631/FITEE.1800562


Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment


Author(s):  Da-qi Zhu, Yun Qu, Simon X. Yang

Affiliation(s):  Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai 201306, China; more

Corresponding email(s):   zdq367@aliyun.com, syang@uoguelph.ca

Key Words:  Autonomous underwater vehicles, Self-organizing neural networks, Azimuths, Ocean current


Da-qi Zhu, Yun Qu, Simon X. Yang. Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 330-341.

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Abstract: 
There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

基于AUV初始方向角和海流环境的SOM任务分配算法

摘要:实际水下环境存在海流。本文针对多自治机器人任务分配系统提出一个改进的自组织神经网络算法。该算法充分考虑自治水下机器人初始方向角和海流环境。每个自治水下机器人都参与竞争。选出实际航行路径最短的自治水下机器人作为获胜神经元,同时确保总航行路径最短。首先,初始化每个自治水下机器人的位置与方向角以及海流流速与方向。其次,通过竞争,选择海流环境下最短航行路径的水下机器人作为获胜神经元,并将该获胜神经元分配给相应目标点。为证明该算法有效性,给出相应仿真结果。

关键词:自治水下机器人;自组织神经网络;初始方向角;海流

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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