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水下声通信网络多自主水下航行器数据采集系统研究

Research on Multi-AUVs Data Acquisition System of Underwater Acoustic Communication Network.

作者信息

Gao Chunxian, Hu Wenwen, Chen Keyu

机构信息

Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, China.

出版信息

Sensors (Basel). 2022 Jul 6;22(14):5090. doi: 10.3390/s22145090.

DOI:10.3390/s22145090
PMID:35890771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9320781/
Abstract

In order to meet the needs of large-scale underwater operations, the underwater acoustic communication network emerged, marking a historic moment. At the same time, the development of artificial intelligence has promoted the application of intelligent underwater robots in large-scale underwater operations, and the research on related algorithms has been gradually promoted. Due to the complexity of underwater operations and the difficulty of replacing batteries, the energy efficiency of intelligent underwater robots is particularly important in multi-AUVs data acquisition systems. In view of the energy consumption of multi-AUVs data acquisition systems in water acoustic cluster networks, this paper proposed the AE (A*-Energy) algorithm for multi-AUVs task assignment and path planning. Through the simulation experiment, it was proved that the AE algorithm proposed in this paper can effectively reduce the energy consumption of multi-AUVs data acquisition systems and has good energy efficiency.

摘要

为满足大规模水下作业的需求，水下声通信网络应运而生，标志着一个历史性时刻。与此同时，人工智能的发展推动了智能水下机器人在大规模水下作业中的应用，相关算法的研究也在逐步推进。由于水下作业的复杂性以及更换电池的困难性，智能水下机器人的能量效率在多自主水下航行器（AUV）数据采集系统中尤为重要。针对水声集群网络中多AUV数据采集系统的能量消耗问题，本文提出了用于多AUV任务分配和路径规划的AE（A*-能量）算法。通过仿真实验证明，本文提出的AE算法能够有效降低多AUV数据采集系统的能量消耗，具有良好的能量效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/4eeb156d1ec8/sensors-22-05090-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/cc2052fa4c04/sensors-22-05090-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/de90bd44a6a4/sensors-22-05090-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/3918132d57c1/sensors-22-05090-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/4eeb156d1ec8/sensors-22-05090-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/cc2052fa4c04/sensors-22-05090-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/f501976e380d/sensors-22-05090-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/f2f084cecec2/sensors-22-05090-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/a538592f4bac/sensors-22-05090-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/a42daf836bf3/sensors-22-05090-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/dc98afd49ebe/sensors-22-05090-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/de90bd44a6a4/sensors-22-05090-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/3918132d57c1/sensors-22-05090-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f55d/9320781/4eeb156d1ec8/sensors-22-05090-g009.jpg

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本文引用的文献

1
Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles.基于多伪Q学习的确定性策略梯度自主水下航行器跟踪控制方法
IEEE Trans Neural Netw Learn Syst. 2019 Dec;30(12):3534-3546. doi: 10.1109/TNNLS.2018.2884797. Epub 2018 Dec 28.
2
A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario.启发式分布式任务分配方法在多车多任务问题中的应用及其在搜索和救援场景中的应用。
IEEE Trans Cybern. 2016 Apr;46(4):902-15. doi: 10.1109/TCYB.2015.2418052. Epub 2015 Apr 13.
3
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.
基于改进的自组织映射和速度合成方法的三维水下作业空间中多 AUV 系统的动态任务分配与路径规划。
IEEE Trans Cybern. 2013 Apr;43(2):504-14. doi: 10.1109/TSMCB.2012.2210212. Epub 2013 Mar 7.