Robot navigation method based on pre-processing layer and deep enhanced learning

Abstract

The invention relates to a robot navigation method based on a pre-processing layer and deep enhanced learning. According to the robot navigation method, a virtual pre-processing layer is arranged in aconstructed virtual training environment, and a realistic pre-processing layer is arranged in a realistic environment; and information with the same significance is output by the realistic pre-processing layer and the virtual pre-processing layer, thus a deep enhanced learning result in the virtual training environment is transplanted onto a robot navigation system in the realistic environment soas to realize navigation. The problem of poor generalization performance in the process that a navigation algorithm based on deep enhanced learning is transferred from the virtual environment to therealistic environment is solved, the pre-processing layer and deep enhanced learning are combined, environment information is received though the pre-processing layer and deep enhanced learning, accurate actions are output, thus the robot carrying the robot navigation method obtains the navigation ability and has the high ability to transfer from the virtual environment to the realistic environment, and the robot navigation method can be applied to the field of robot navigation.

Description

technical field [0001] The invention relates to the technical field of robot navigation, in particular to a robot navigation method based on a preprocessing layer and deep reinforcement learning. Background technique [0002] In the past two decades, robots have become more common and more important in many human activities. [0003] However, due to the complex and unpredictable environment, most robots realize their navigation through manual or semi-automatic operation. Although it provides the possibility to deal with unforeseen environmental conditions. But humans are needed to understand the sensory data obtained by the sensors and make decisions to drive the robot. Therefore, mobile robots require navigation systems with higher levels of intelligence and autonomy to allow them to autonomously make optimal decisions in complex environments. [0004] In deep reinforcement learning, the robot interacts with the environment, that is, by performing actions in the environm...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the defects or deficiencies in the prior art, the present invention provides a robot navigation method based on preprocessing layer and deep reinforcement learning to solve the problem of poor generalization performance of the navigation algorithm based on deep reinforcement learning from the virtual environment to the real environment , combine the preprocessing layer with deep reinforcement learning, receive environmental information and output correct actions through the preprocessing layer and deep reinforcement learning, and then enable the robot equipped with the above method to obtain navigation capabilities and have a strong ability to migrate from the virtual environment to reality The ability of the environment can be applied to the field of robot navigation

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