Method and system for controlling underwater robot locus based on deep reinforcement learning

Abstract

The present invention discloses a method and system for controlling an underwater robot locus based on deep reinforcement learning. The system comprises a learning phase and an application phase. In the learning phase, a simulator simulates the running process of an underwater robot and collects the data of the simulated running underwater robot, the data comprises the state of each moment and the target state of the next moment corresponding to each moment, and the learning is performed aiming at a decision neural network, an auxiliary decision neural network, an evaluation neural network and an auxiliary evaluation neural network through the data. In the application phase, the state o the underwater robot at the current moment and the target state of the underwater robot at the next moment are obtained and input to the decision neural network obtained through the final learning in the learning phase, and the decision neural network is configured to calculate the propulsive force required by the underwater robot at the current moment. The method and system for controlling underwater robot locus based on the deep reinforcement learning can realize accurate control of the underwater motion track.

Description

Trajectory control method and control system of underwater robot based on deep reinforcement learning technical field The invention relates to underwater robot control technology, in particular to an underwater robot trajectory control method and control system based on deep reinforcement learning. Background technique In recent years, underwater robots have been widely used in many marine science fields such as ocean exploration and marine environmental protection, and their status has become increasingly important. Through precise control of the trajectory of underwater robots, people can safely complete some tasks with relatively high risk factors. High-level tasks, such as exploring seabed oil and repairing seabed pipelines, etc. At present, there is still a common way for underwater robots to complete tasks on a specified trajectory manually. Manual operation requires a lot of energy and labor intensity, especially when the water flow When there is a change or externa...

AI Technical Summary

Problems solved by technology

The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a trajectory control method for underwater robots based on deep reinforcement learning. Through this control method, the precise control of the tr

Images