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一种基于UKF的水下机器人状态和参数联合估计方法
Alternative TitleUnscented Kalman filter (UKF)-based underwater robot state and parameter joint estimation method
刘开周; 程大军; 李一平; 封锡盛
Department水下机器人技术研究室
Rights Holder中国科学院沈阳自动化研究所
Patent Agent沈阳科苑专利商标代理有限公司 21002
Country中国
Subtype发明
Status有权
Abstract本发明公开一种基于UKF的水下机器人状态和参数联合估计方法,该方法首先建立了水下机器人的扩展参考模型包括水下机器人的动力学模型和推进器的故障模型。本发明依据位置传感器探测到的位姿信息,采用UKF算法,以水下机器人状态包括位姿和速度及推进器故障参数,对扩展参考模型进行在线联合估计,实时估计出水下机器人的速度信息和推进器故障信息。该方法具有很好的实时性,可在线对系统的状态和参数进行联合估计;当过程噪声和测量噪声的先验信息已知的情况下,该方法能够达到较高的估计精度。
Other AbstractThe invention discloses an unscented Kalman filter (UKF)-based underwater robot state and parameter joint estimation method. According to the method, expansion reference models of an underwater robot are established, and comprise a kinetic model of the underwater robot and a fault model of a propeller. According to pose information detected by a position sensor, the expansion reference models are subjected to on-line joint estimation through states of the underwater robot, including pose and speed, and propeller fault parameters by a UKF algorithm, and the speed information of the underwater robot and the propeller fault information are estimated in real time. The method has a high real-time property, and the states and parameters of a system can be subjected to joint estimation and under the condition that prior information of process noise and measurement noise is known, high estimation accuracy can be achieved by the method.
PCT Attributes
Application Date2011-05-25
2012-11-28
Date Available2015-03-11
Application NumberCN201110137339.2
Open (Notice) NumberCN102795323A
Language中文
Contribution Rank1
Document Type专利
Identifierhttp://ir.sia.cn/handle/173321/13089
Collection水下机器人研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
刘开周,程大军,李一平,等. 一种基于UKF的水下机器人状态和参数联合估计方法[P]. 2012-11-28.
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