SIA OpenIR  > 水下机器人研究室
Alternative TitleResearch on the integrated navigation of full ocean depth HOV
Thesis Advisor李智刚
Keyword水下机器人 全海深载人潜水器 组合导航 异步融合 机器学习
Degree Discipline控制工程
Degree Name专业学位硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract目前,随着科学技术的发展,对深海的探索和研究已经成为海洋科学领域的研究热点,由此也促进了载人潜水器(HOV)向全海深方向发展。HOV已经在海洋生物、海洋科学、海洋资源等领域发挥着越来越重要的重要。为了进一步满足海洋科学家研究全海深海洋环境的迫切需要,全海深HOV的研究工作已逐步展开,全海深HOV的工作环境对水下高精度实时导航定位系统提出了新的挑战。本文以“十三五”国家重点研发计划——全海深载人潜水器控制技术研究课题为依托,深入分析了全海深载人潜水器的应用背景和存在的问题,研究了载人潜水器组合导航系统以及导航融合算法。本文从数据、模型、方法层面详细阐述了载人潜水器组合导航系统,揭示了全海深载人潜水器组合导航系统问题产生机制,提出了问题解决算法,试验验证了所提出的算法能够提高全海深载人潜水器组合导航系统的定位精度,对HOV的轨迹跟踪控制具有重要意义。本文研究内容可归纳总结如下:1、本文首先分析了全海深载人潜水器组合导航系统的应用背景,给出了载人潜水器组合导航系统存在的问题,包括噪声问题和异步融合问题,并阐述了问题产生的原因。2、 从数据方面、模型方面、融合算法方面分析了载人潜水器组合导航系统,详细阐述了卡尔曼滤波算法、扩展卡尔曼滤波、无迹滤波、粒子滤波算法在数据滤波方面的优缺点,为载人潜水器组合导航定位算法的提出垫底了理论基础。3、 针对全海深载人潜水器出现的噪声问题进行了相关研究,提出一种基于BOR重采样算法的粒子滤波算法,仿真验证了所提出的算法可有效抑制超短基线的噪声,提高了导航定位精度。4、针对全海深载人潜水器出现的异步融合问题进行了相关研究,提出了一种基于机器学习和无迹卡尔曼滤波的组合导航定位算法LR-UKF,并通过仿真试验验证了所提算法的可行性,并与DF-UKF进行了比较,试验结果表明,本文提出的算法定位性能优于DF-UKF算法。
Other AbstractAt present, with the development of science and technology, exploration and research of the deep sea in the scientific field has become a hot spot. So the development of human occupied vehicle(HOV) has been accelerated. HOV has played an increasingly important role in marine biology, marine science and marine resource. In order to further meet the urgent needs of scientists in full ocean depth field, the research of full ocean depth HOV has been gradually carried out. However, the demand of underwater high-precision real-time navigation of HOV also brings new challenges. This thesis is based on the subject of full ocean depth HOV control technology research in National key R&D plan for the 13th five-year plan. In this paper, the integrated navigation system and the data fusion algorithm of underwater robot are studied systematically. Then the background and environment of navigation of full ocean depth HOV and the problems of noise and asynchronous fusion are deeply analysed. After the three aspects of integrated navigation, including data, model and method, this thesis makes a careful study on the problems of integrated navigation system of full ocean depth HOV and proposed algorithms to solve the problems. These algorithms that can raise the accuracy of the integrated navigation system and provide accurate navigation information have been certified by simulation experiment. It has important significance for tracking and controlling the track of HOV. The main contents of this thesis are as follow: 1、 Firstly, the integrated navigation system and background of full ocean depth HOV are analysed and the noise problems and asynchronous fusion problems are pointed out. Then the causes of these problems are studied. 2、 Three aspects of integrated navigation system of underwater robot are studied, which are data aspect, model aspect and method aspect of fusion algorithm respectively. The advantage and unadvantage of Kalman filter, extended Kalman filter, unscented Kalman filer and particle filter are analysed, which provide the theoretical basis for proposing integrated navigation algorithms of full ocean depth HOV. 3、A particle filter algorithm based on BOR resampling algorithm was proposed after studying the noise problem of full ocean depth HOV. Then the simulation manifests its effect on noise problems and improving accuracy of navigation. 4、A LR-UKF algorithm based on machine learning and UKF is proposed after studying the asychronouos fusion problem of full ocean depth HOV. And the feasibility of the algorithm is vertified by simulation. Then comparing with the DF-UKF, the accuracy of integrated navigation algorithm is more prominent.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
张志慧. 全海深载人潜水器组合导航算法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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