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Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184 (print), ISSN 2095-9230 (online)

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

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.

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

Chinese Summary  <20> 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。

关键词组:自治水下机器人;自组织神经网络;初始方向角;海流


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DOI:

10.1631/FITEE.1800562

CLC number:

TP249

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On-line Access:

2019-04-09

Received:

2018-09-18

Revision Accepted:

2019-02-01

Crosschecked:

2019-03-14

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