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水下洋流环境下的两型六足水下机器人的同步定位与导引

Simultaneous Localization and Guidance of Two Underwater Hexapod Robots under Underwater Currents.

机构信息

Defense System Engineering Department, Sejong University, Seoul 05006, Republic of Korea.

出版信息

Sensors (Basel). 2023 Mar 16;23(6):3186. doi: 10.3390/s23063186.

DOI:10.3390/s23063186
PMID:36991902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10054550/
Abstract

This paper addresses the simultaneous localization and guidance of two underwater hexapod robots under sea currents. This paper considers an underwater environment where there are no landmarks or features which can assist a robot's localization. This article uses two underwater hexapod robots that move together while using each other as landmarks in the environment. While one robot moves, another robot extends its legs into the seabed and acts as a static landmark. A moving robot measures the relative position of another static robot, in order to estimate its position while it moves. Due to underwater currents, a robot cannot maintain its desired course. Moreover, there may be obstacles, such as underwater nets, that a robot needs to avoid. We thus develop a guidance strategy for avoiding obstacles, while estimating the perturbation due to the sea currents. As far as we know, this paper is novel in tackling simultaneous localization and guidance of underwater hexapod robots in environments with various obstacles. MATLAB simulations demonstrate that the proposed methods are effective in harsh environments where the sea current magnitude can change irregularly.

摘要

本文针对海流环境下的两个水下六足机器人的同时定位与导航问题进行了研究。该文考虑了一种水下环境,其中没有可以辅助机器人定位的地标或特征。本文使用两个水下六足机器人,它们在环境中一起移动,并将彼此作为地标。当一个机器人移动时,另一个机器人会将其腿伸向海底并充当静态地标。移动机器人测量另一个静态机器人的相对位置,以便在移动时估计其位置。由于海流的影响,机器人无法保持其期望的航向。此外,可能存在障碍物,例如水下网,机器人需要避开这些障碍物。因此,我们开发了一种用于避免障碍物的导航策略,同时估计海流引起的扰动。据我们所知,本文在处理具有各种障碍物的水下六足机器人的同时定位和导航方面具有新颖性。MATLAB 仿真结果表明,所提出的方法在海流强度可能不规则变化的恶劣环境中是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebe6/10054550/8f6125be49e9/sensors-23-03186-g015.jpg
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本文引用的文献

1
Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images.基于前视声纳图像的自主水下航行器渔网检测多感受野网络(MRF-Net)
Sensors (Basel). 2021 Mar 10;21(6):1933. doi: 10.3390/s21061933.
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Underwater Localization System Combining iUSBL with Dynamic SBL in ¡VAMOS! Trials.水下定位系统将 iUSBL 与动态 SBL 相结合在 ¡VAMOS! 试验中。
Sensors (Basel). 2020 Aug 20;20(17):4710. doi: 10.3390/s20174710.
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Multirobot Exploration While Building Power-Efficient Sensor Networks in Three Dimensions.
在三维空间构建节能传感器网络时的多机器人探索
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