Autonomous underwater vehicle track predicating method based on marine environment information

A track prediction and underwater submersible technology, which is applied in the field of autonomous underwater submersible track prediction, can solve the problems of large differences in stress conditions of different individuals and the lack of extensive application value of mathematical model methods

Active Publication Date: 2015-01-07
HARBIN ENG UNIV
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Problems solved by technology

Due to the small number of data points and the large difference in the stress of different individuals in the tr...

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  • Autonomous underwater vehicle track predicating method based on marine environment information
  • Autonomous underwater vehicle track predicating method based on marine environment information
  • Autonomous underwater vehicle track predicating method based on marine environment information

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Embodiment Construction

[0070] The following will be combined with figure 1 ~ attached image 3 The present invention is described in further detail.

[0071] The invention proposes an autonomous underwater submersible track prediction method based on ocean environment information. Compared with the traditional method of establishing kinematics or dynamics equations for track prediction, the present invention is characterized in that: firstly, the BP neural network prediction model is trained using prior data, and the successfully trained model is used for track prediction , which solves the problem that the establishment of dynamic equations is complicated and difficult to solve due to the large randomness of AUV underwater motion. In addition, the present invention uses marine environment data together with AUV position and control data as the input samples of the BP neural network prediction model, so that the BP neural network can learn more fully, thereby improving the prediction accuracy of t...

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Abstract

The invention discloses an autonomous underwater vehicle (AUV) track predicating method based on marine environment information. According to the method, the current track data of an AUV are acquired in real time, the current track data of the AUV are input into an AUV track predicting model for prediction, and then the predicated track of the AUV is obtained. The predicating method of the AUV track predicating model comprises the steps of forming a training sample of the AUV track predicating model, conducting principal component analysis on input data to obtain simplified input data, determining calculating parameters and establishing the AUV track predicating model of the BP neural network, and conducting BP neural network training on the simplified input data to obtain the weight values and threshold values of all layers of the AUV track predicating model. According to the method, measured AUV position data and marine environment data serve as the input of the track predicating model together, so that data are more complete, and then track predicting precision is improved.

Description

technical field [0001] The invention belongs to the field of autonomous underwater submersible track prediction, in particular to an autonomous underwater submersible track prediction method based on ocean environment information. Background technique [0002] Autonomous Underwater Vehicle (AUV) is an important tool to complete various underwater intelligent tasks. Facing the rapid development of ocean development and the urgent needs of the world's military, AUV has been greatly developed and widely used. Applications. Due to the complexity of the marine environment, in order for AUV to have the ability to avoid harmful objects when it is disturbed by sudden factors, it is necessary to be able to grasp the current navigation situation and predict the navigation trajectory. The most effective way to solve this problem is to predict the trajectory. [0003] Trajectory prediction was first applied in the field of aviation to predict the future motion state of the aircraft bas...

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Application Information

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IPC IPC(8): G06N3/02G06N3/08
Inventor 刘厂杨淳赵玉新高峰刘利强韩桂军李威
Owner HARBIN ENG UNIV
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