Deep reinforcement learning-aided autonomous navigation with landmark generators

Frontiers in Neurorobotics

Neuroscience

Xuanzhi Wang¹  Yankang Sun¹  Yuyang Xie¹  Jiang Bin¹  Jian Xiao² 

[1] Department of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China;null;

关键词: deep reinforcement learning (DRL);    mobile robots;    trajectory planning;    autonomous navigation;    simultaneous localization and mapping;   

DOI  :  10.3389/fnbot.2023.1200214

 received in 2023-04-04, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL's training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Sun, Xie, Bin and Xiao.