SIA OpenIR  > 水下机器人研究室
Alternative TitleResearch and implementation of small AUV integrated navigation system based on multi-sensor information fusion
Thesis Advisor李硕
Keyword小型自主水下机器人 组合导航系统 卡尔曼滤波 导航方案
Call NumberU675.2/S57/2017
Degree Discipline控制工程
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract一个精确的、鲁棒性强的导航系统是小型自主水下机器人(AUV, autonomous underwater vehicle)实现自主导航和精确定位的关键。而AUV导航难点在AUV工作于水下,不能接收GPS卫星信号;同时其工作环境复杂,具有强非线性。为此,AUV常采用水下组合导航方式,通过数据融合得到最优导航信息。为进一步提高AUV导航精度,本文设计了以捷联惯性导航系统为主系统,以DVL、GPS和TCM为校正系统的组合导航方式,并结合AUV低功耗和高集成度的设计要求,在I.MX6Q处理器和MOOS软件框架中实现嵌入式组合导航。具体研究分为以下三部分。 (1)首先,针对AUV导航系统对三种滤波方法进行了研究: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)。并利用数湖试数据对算法进行验证和对比,为导航系统的设计提供了理论基础。 (2)其次,设计了以捷联惯性导航系统为主,以DVL、GPS、TCM为校正系统的组合导航系统,给出了组合导航系统结构图。对构成导航系统的误差源进行了分析,建立了传感器误差模型。对导航系统的滤波器进行了研究,设计了以误差为状态变量的非线性滤波器。最后通过仿真实验对设计的导航系统进行了全面分析。 (3)最后,设计了基于ARM的小型AUV组合导航方案,根据实际搭载传感器需求,对I.MX6Q核心板进行了外围接口设计。同时使用开源套件MOOS平台作为导航系统应用程序,对MOOS系统架构进行研究,实现了导航传感器的数据采集功能,并利用该平台验证了该导航系统的可行性。
Other AbstractA precise and robust navigation system is the key to autonomous navigation and accurate positioning of a small autonomous underwater vehicle (AUV, autonomous underwater vehicle). While AUV navigation is difficulty to work underwater, and it often lacks GPS satellite signals. At the same time, it’s working environment is complex and has strong nonlinearity. Therefore, underwater integrated navigation is often used in AUV, and the optimal navigation information is obtained by data fusion. This paper combined with the design requirements of low power consumption and high integration of AUV, the integrated navigation system based on strapdown inertial navigation system (SINS), DVL, GPS and TCM as correcting system is designed, and the embedded integrated navigation is implemented in the I.MX6Q processor and MOOS software framework, the specific research is divided into the following three parts. (1) Firstly, the filtering method used in the navigation system is studied. Study on three kinds of filtering method of data fusion method: extended Kalman filter (referred to as EKF), Unscented Kalman Filter (referred to as UKF) and Adaptive Unscented Kalman filter (called AUKF). Several Lake trial data are used to validate and compare the algorithm, provide a theoretical basis for the design of navigation system. (2) Secondly, the integrated navigation system based on strapdown inertial navigation system (SINS), DVL, GPS and TCM as correcting system is designed, and the structure of integrated navigation system is given. The error sources of navigation system are analyzed, and the sensor error model is established. The filter of navigation system is studied and a nonlinear filter with error as state variable is designed. Finally, through the simulation experiment, the designed navigation system is comprehensively analyzed. (3) Finally, designed the small AUV integrated navigation scheme based on ARM, and designed the peripheral interface of the I.MX6Q core board according to the actual needs of sensors. At the same time, the use of open source MOOS platform suite as a navigation system application, the research on MOOS system architecture, realizes the data acquisition function of navigation sensors, and use the platform to verify the feasibility of the navigation system.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
史兴波. 基于多传感器信息融合的小型AUV组合导航系统研究与实现[D]. 沈阳. 中国科学院沈阳自动化研究所,2017.
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