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SVR辅助SINS-DVL的水下机器人组合导航方法
Alternative TitleSVR assisted SINS-DVL underwater robot integrated navigation method
魏奥博1,2,3; 郑荣1,2
Department海洋信息技术装备中心
Source Publication舰船科学技术
ISSN1672-7649
2020
Volume42Issue:1Pages:161-167
Contribution Rank1
Funding Organization中国科学院装备预研联合基金资助项目(6141A01061601)
KeywordSVR SINS-DVL 组合导航 DVL数据无效
Abstract

多普勒计程仪(DVL)在水下导航系统应用越来越广泛。当海底环境发生变化时,DVL会发生数据刷新频率不稳定,数据无效等情况。为了提高导航的可靠性,本文提出了一种变训练集的SVR回归方法,对DVL的数据进行预测。根据水下机器人的速度变化率和加速度变化率调节训练集大小。把捷联惯导(SINS)的东向和北向速度作为输入,DVL东向和北向速度作为输出对模型进行训练。根据SINS的数据输出频率,选取合适的预测数据输出频率进行仿真。仿真发现算法有效地提高了SINS/DVL组合导航的精度,并在DVL数据无效时,有效地抑制误差,提高导航系统的稳定性。

Other Abstract

DVL is more and more widely used in underwater navigation system. When the seabed environment changes, the data refresh frequency of DVL is unstable and the data is invalid. In order to improve the reliability of navigation, this paper proposes a SVR regression method of variable training set to predict DVL data. The training set size is adjusted according to the change of velocity rate and acceleration rate of underwater robot. Taking the east and north velocity of SINS as input, DVL's east and north velocity as output, the model is trained. We select the appropriate predictive data output frequency that relys on the data out frequency of SINS. It is found that SINS/DVL integrated navigation precision is effectively improved through simulation. When the DVL data is invalid, the error is effectively suppressed and the stability of the navigation system is improved.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26434
Collection海洋信息技术装备中心
Corresponding Author魏奥博
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
Recommended Citation
GB/T 7714
魏奥博,郑荣. SVR辅助SINS-DVL的水下机器人组合导航方法[J]. 舰船科学技术,2020,42(1):161-167.
APA 魏奥博,&郑荣.(2020).SVR辅助SINS-DVL的水下机器人组合导航方法.舰船科学技术,42(1),161-167.
MLA 魏奥博,et al."SVR辅助SINS-DVL的水下机器人组合导航方法".舰船科学技术 42.1(2020):161-167.
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