Visual-Based Deep Reinforcement Learning for Mobile Robot Obstacle Avoidance Navigation
- Title
- Visual-Based Deep Reinforcement Learning for Mobile Robot Obstacle Avoidance Navigation
- Author
- 남해운
- Keywords
- real-world; deep reinforcement learning; indoor navigation
- Issue Date
- 2023-10
- Publisher
- IEEE
- Citation
- 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), page. 440-445
- Abstract
- To address the issue of navigation failure caused by light reflection in real-world navigation scenarios using inexpensive 2D LiDARs, traditional SAC-based algorithms face challenges such as inability to train in highly randomized and sparsely rewarded environments, as well as slow training. In this paper, we propose a combination of a monocular camera and a depth estimation model as a substitute for the inexpensive 2D LiDAR and introduce a variant algorithm called Sharing Encoder Self-Attention Soft Actor Critic (SESA-SAC) for collision-free indoor navigation of mobile robots. To improve the efficiency of robot learning in sparse environments, we collect expert data from 200 episodes and store them in a replay buffer. We conduct training by randomly sampling from both exploration data and expert data, without pre-training. To enhance training performance, we introduce a channel-wise self-attention structure and layer normalization in the network to learn better features. Additionally, we propose a shared feature extractor to achieve more stable training. Moreover, we conduct training and testing in GAZEBO, and the experimental results demonstrate that our proposed SESA-SAC algorithm outperforms traditional SAC algorithms in terms of convergence speed, stability, and efficiency for indoor navigation tasks.
- URI
- https://ieeexplore.ieee.org/document/10393297https://repository.hanyang.ac.kr/handle/20.500.11754/190723
- ISSN
- 2162-1241; 2162-1233
- DOI
- 10.1109/ICTC58733.2023.10393297
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML