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基于声学定位的HOV组合导航算法研究与实现
其他题名Research and Implementation of Navigation of HOV Based on Acoustic Positioning
李静1,2
导师刘开周
分类号TP242
关键词载人潜水器 组合导航 扩展卡尔曼滤波 无色卡尔曼滤波 自适应无色卡尔曼滤波 Google Earth
索取号TP242/L32/2013
页数57页
学位专业模式识别与智能系统
学位名称硕士
2013-05-28
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门水下机器人研究室
摘要在水下机器人作业领域,载人潜水器(Human Occupied Vehicle,简称HOV)使得人类亲自探求深海奥秘的梦想得以实现,也使人类的智慧在深海作业中得到最及时充分的发挥。然而HOV对海洋的精确作业也对水下导航技术提出了更高的要求。水声定位法通过确定潜水器相对于海底应答器的位置,能够实现潜水器的高精度定位导航。 中科院沈阳自动化所参与研制了蛟龙号7000米载人潜水器的控制系统,其中的导航系统前期采用了超短基线声学定位装置,后期采用了长基线声学定位装置,正在开展研究的4500米载人潜水器计划采用长基线声学定位装置,因此依托项目背景需要,对基于超短基线和长基线导航系统的研究具有很大的实际应用价值和学术价值。在往次海试实验数据的基础上,本文分别研究了基于超短基线、基于长基线的组合导航两种导航系统,该导航系统融合声学定位装置、运动传感器、多普勒计程仪的数据为HOV提供精确的位置、速度和姿态信息,为HOV的安全航行和有效作业提供必要、有效的信息。 本文首先介绍了水下机器人组合导航系统的研究现状;分析了载人潜水器的应用背景、结构特点;根据载人潜水器导航系统的构成设计了组合导航方案;针对数据融合方法研究了三种滤波方法:扩展卡尔曼滤波(Extended Kalman Filter,简称EKF)、无色卡尔曼滤波(Unscented Kalman Filter,简称UKF)和自适应无色卡尔曼滤波(Adaptive Unscented Kalman Filter,简称AUKF);根据不同的应用背景和组合导航系统,基于往次海试实验数据对比分析了三种滤波方法的实验结果和优缺点;基于Google Earth的二次开发,编写了组合导航算法的应用软件。 本文设计的组合导航算法不仅适用于深海载人潜水器的导航,对于其它水下机器人也有借鉴和指导意义。
其他摘要The dream has come true in the field of underwater vehicles operating, and human occupied vehicle (HOV) not only enables the human person to explore the mysteries of the deep sea, but also makes the human person’s intelligence get the most timely and fully exploited in deep-sea operations. However, HOV’s precise operation of the marine underwater puts forward higher requirements to navigation technology. Acoustic positioning method can reach high-precision positioning and navigation by determining the submersible relative to the position of the bottom transponders. Shenyang Institute of Automation, Chinese Academy of Sciences participate the development of the control system of the HOV named “Jiaolong”, and this HOV’s combined navigation system used ultra-short baseline positioning system in early days and uses long baseline in later period. Another HOV which is in development for 4500m plans to equip with long baseline. So research of navigation of HOV based on acoustic positioning has great practical value and academic value. On the basis of experimental data of sea trial, two combined navigation system is studied: one based on the long baseline and the other one based on the ultra-short baseline. By integrating data from acoustic positioning device, motion sensors, DVL and depth gauge, the navigation system can provide necessary, valid information for the safe navigation and the effective operation of the HOV. Firstly, the research status of the underwater vehicle integrated navigation system is introduced, and the application background and structural characteristics of HOV is analyzed. Secondly, the scheme of combined navigation system is designed, and three data fusing methods are studied, which includes extended Kalman filter (EKF), unscened Kalman filter (UKF) and adaptive unscened Kalman filter (AUKF). Then the comparative analysis of the experimental results and the advantages and disadvantages of the three methods of filtering is made according to the data of the field tests. Finally, combined navigation algorithm application software is developed based on Google Earth. The algorithm of combined navigation proposed by this paper applies not only to the deep-sea manned submersibles navigation, but also to other underwater vehicles.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/10789
专题水下机器人研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
李静. 基于声学定位的HOV组合导航算法研究与实现[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
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