Research on Target-Driven Navigation of Mobile Robot Based on Deep Reinforcement Learning and Preprocessing Layer

Abstract

Recently, with the rise of deep reinforcement learning model, robot navigation based on this method has a huge advantage compared with traditional slam method, which has attracted extensive attention. However, when the navigation algorithm trained in the virtual environment is transferred to the real environment, the navigation performance of the robot will decline sharply because of the great difference between the virtual environment and the real environment. In order to improve the navigation ability of mobile robot, this paper implements a mobile robot navigation system based on deep reinforcement learning without environment map and only visual input. At the same time, in order to solve the problem of poor generalization ability of deep reinforcement learning from virtual environment to real environment, this paper proposes a preprocessing layer with knowledge and combines it with deep reinforcement learning module. The combined algorithm model alleviates the performance fault problem caused by the migration algorithm and the performance difference between virtual sensor and real sensor. At the end of this paper, a navigation experiment based on the turtlebot is designed, which proves that the deep reinforcement learning algorithm with the preprocessing layer can alleviate the performance fault problem caused by the migration algorithm, and have a certain ability of obstacle avoidance and avoidance without the environment map.

Research on target recognition and mobile job control of humanoid robot based on KM34Z256 for industrial applications

“…Without changing the working environment, the humanoid robot can complete the mobile operation efficiently and steadily, which makes the humanoid robot have a strong application prospect and social and economic value. At present, humanoid robot has become the core in the robot field and occupies a crucial position in the research and development projects (Yu et al , 2020) (see Table 1).…”