Deep reinforcement learning control method for vertical path following of intelligent underwater robot

Abstract

The invention provides a deep reinforcement learning control method for vertical path following ofan intelligent underwater robot. The deep reinforcement learning control method comprises the following steps that firstly, according to path following control requirements of the intelligent underwater robot, an intelligent underwater robot environment is established to interact with an agent; secondly, an agent set is established; thirdly, an experience buffer pool is established; fourthly, a learner is established; and fifthly, intelligent underwater robot path following control is conducted byusing a distributed deterministic strategy gradient. According to the deep reinforcement learning control method for the vertical path following ofthe intelligent underwater robot, the deep reinforcement learning control method for the vertical path following ofthe intelligent underwater robot is designed to solve the problem that marine environment in which the intelligent underwater robot is located is complex and variable, thus a traditional control method can not interact with the environment. The path followingand control task of the intelligent underwater robot can be finished in a distribution mode by using a deterministic strategy gradient, and the deep reinforcement learning control method for the vertical path following ofthe intelligent underwater robot has the advantages of self-learning, high precision, good adaptability and stable learning process.

Description

technical field [0001] The invention relates to a control method of an underwater vehicle, in particular to a deep reinforcement learning control method for vertical surface path following of an intelligent underwater robot. Background technique [0002] With the deepening of marine development, intelligent underwater robots have been widely used in marine environmental protection and marine resource development due to their flexible movement, convenient portability, and autonomous operation, and their status has become increasingly important. In addition, the precise control of intelligent underwater robots makes some extremely dangerous tasks safe, such as exploring subsea oil, repairing subsea pipelines, and tracking and recording the location of explosive materials. [0003] Traditional path-following control methods such as fuzzy logic control, PID control, and S-plane control require artificial adjustment of control parameters, and the control effect depends on human e...

AI Technical Summary

Problems solved by technology

[0003] Traditional path-following control methods such as fuzzy logic control, PID control, and S-plane control require artificial adjustment of c

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