| Autor: |
Chen Wang, Xudong Li, Xiaolin Tao, Kai Ling, Quhui Liu, Gan Tao |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Journal of Robotics, Vol 2021 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1687-9619 |
| DOI: |
10.1155/2021/3028319 |
| Popis: |
Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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