UUV (unmanned underwater vehicle) dynamic planning method based on LSTM-RNN (long short term memory-recurrent neural network)

A technology of dynamic planning and ant colony algorithm, applied in the field of unmanned underwater vehicles, can solve problems such as the contradiction between the accuracy and the real-time path optimization degree of the environmental model.

Active Publication Date: 2018-07-13
HARBIN ENG UNIV
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Problems solved by technology

However, the real-time path planning system based on the above-mentioned traditional algorithm has the problem that the accurac

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  • UUV (unmanned underwater vehicle) dynamic planning method based on LSTM-RNN (long short term memory-recurrent neural network)
  • UUV (unmanned underwater vehicle) dynamic planning method based on LSTM-RNN (long short term memory-recurrent neural network)
  • UUV (unmanned underwater vehicle) dynamic planning method based on LSTM-RNN (long short term memory-recurrent neural network)

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[0105] Further describe the present invention below in conjunction with accompanying drawing:

[0106] A UUV dynamic programming method based on LSTM-RNN, comprising the following steps:

[0107] Step (1): Select the geometric model to construct the obstacle environment model:

[0108] For the two-dimensional geometric model, following the principle of "using the least amount of data and describing the most complete information", the obstacles are divided into two types: elliptical or circular obstacles and polygonal obstacles.

[0109] For elliptical or circular obstacles, store the coordinates of two diagonal points of elliptical or circular obstacles, and use these two point coordinates to calculate the center of the ellipse and the long and short radii to obtain all information about elliptical or circular obstacles.

[0110] For polygonal obstacles, the coordinates of the polygonal vertices are stored. Starting from any vertex, the polygonal vertices are stored in a sequ...

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Abstract

The invention discloses a UUV (unmanned underwater vehicle) dynamic planning method based on an LSTM-RNN (long short term memory-recurrent neural network), and belongs to the field of unmanned underwater vehicles. The UUV dynamic planning method includes the steps: (1) selecting a geometric model to build an obstacle environment model; (2) building a UUV dynamic planner for acquiring a data set byan ant colony algorithm; (3) designing an LSTM-RNN model for dynamic planning; (4) acquiring the data set; (5) training the LSTM-RNN by data of a training set in the data set to obtain the dynamic planner based on the LSTM-RNN; (6) inputting sonar detection information and target point information to the dynamic planner based on the LSTM-RNN to obtain the navigational direction and the navigational speed of a UUV at a next time. The method has strong learning capacity and further has quite strong generalization capacity, so that the implemented dynamic planner is applicable to complex environments. The requirement of real-time performance is met, and planned routes conform to movement characteristics of the UUV.

Description

technical field [0001] The invention belongs to the field of unmanned underwater vehicles, and in particular relates to a UUV dynamic programming method based on LSTM-RNN. Background technique [0002] The dynamic path planning capability of UUV in an unknown environment is one of the important indicators reflecting its intelligence level. Traditional dynamic programming methods often suffer from the contradiction between the accuracy of the environment model and the real-time planning, and in a complex environment with a large number of random motion obstacles, it is necessary to design auxiliary strategies to achieve ideal avoidance. The design of these auxiliary strategies is quite complicated , and requires a lot of computing time. Therefore, it is of great theoretical and practical value to search for a simple, cheap, efficient, and easy-to-implement dynamic path planning method. [0003] With the depletion of terrestrial resources, countries have begun to invest a lo...

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Application Information

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IPC IPC(8): G05D1/06G06N3/00G06N3/04G06N3/08
CPCG05D1/0692G06N3/006G06N3/049G06N3/08G06N3/045
Inventor 王宏健林常见么洪飞肖瑶张宏瀚张雪莲
Owner HARBIN ENG UNIV
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