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

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...

AI Technical Summary

Problems solved by technology

However, the real-time path planning system based on the above-mentioned traditional algorithm has the problem that the accuracy of the environment model and the optimization degree of the path are contradictory with the real-time performance of the plan.

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