Robot autonomous navigation path planning based on deep reinforcement learning

Abstract

Due to the rapid development of artificial intelligence, robot technology is now widely used in many fields, and its autonomous navigation capability is receiving increasing attention. As a core component of autonomous navigation, path planning often has limitations in traditional methods in complex environments. Deep reinforcement learning, with its powerful environmental learning and decision-making capabilities, provides a new and effective approach for autonomous navigation path planning of robots. This article deeply explores the basic theory and key technologies of deep reinforcement learning, and analyzes in detail the principles and advantages of applying it to robot autonomous navigation path planning. By comparing the performance of different deep reinforcement learning algorithms in this task through experiments, the ability to achieve efficient path planning in complex and dynamic environments has been verified. It is hoped that this can effectively promote the development and application of robot autonomous navigation path planning technology based on deep reinforcement learning.