Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (5): 575-585.doi: 10.16183/j.cnki.jsjtu.2019.277

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“自动化技术、计算机技术”专题

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A Regionalization Vision Navigation Method Based on Deep Reinforcement Learning

LI Peng, RUAN Xiaogang, ZHU Xiaoqing(), CHAI Jie, REN Dingqi, LIU Pengfei   

  1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  • Received:2019-09-26 Online:2021-05-28 Published:2021-06-01
  • Contact: ZHU Xiaoqing


Aimed at the problems of navigation in distributed environment of a mobile robot, a regionalization vision navigation method based on deep reinforcement learning is proposed. First, considering the characteristics of distributed environment, the independent submodule learning control strategy is used in different regions and the regionalization model is built to switch and combine navigation control strategies. Then, in order to make the robot have a better goal-oriented behavior, reward prediction task is integrated into the submodule, and reward sequence is played back in combination with the experience pool. Finally, depth limitation is added to the primitive exploration strategy to prevent the traversal stagnation caused by collision. The results show that the application of reward prediction and depth obstacle avoidance is helpful to improve navigation performance. In the process of multi-area environment test, the regionalization model shows the advantages that the single model does not have in terms of training time and rewards, indicating that it can better deal with large-scale navigation. In addition, the experiment is conducted in the first-person 3D environment, and the state is partially observable, which is conducive to practical application.

Key words: deep reinforcement learning, distributed environment, regionalization model, reward prediction, depth obstacle avoidance

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