Fast Transfer Navigation for Autonomous Robots
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/3028319 |
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| _version_ | 1832564836387520512 |
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| author | Chen Wang Xudong Li Xiaolin Tao Kai Ling Quhui Liu Gan Tao |
| author_facet | Chen Wang Xudong Li Xiaolin Tao Kai Ling Quhui Liu Gan Tao |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-99aadcdc8ed44f08a6ed4c5c8aa41824 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-99aadcdc8ed44f08a6ed4c5c8aa418242025-02-03T01:10:11ZengWileyJournal of Robotics1687-96192021-01-01202110.1155/2021/3028319Fast Transfer Navigation for Autonomous RobotsChen Wang0Xudong Li1Xiaolin Tao2Kai Ling3Quhui Liu4Gan Tao5School of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringNavigation 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.http://dx.doi.org/10.1155/2021/3028319 |
| spellingShingle | Chen Wang Xudong Li Xiaolin Tao Kai Ling Quhui Liu Gan Tao Fast Transfer Navigation for Autonomous Robots Journal of Robotics |
| title | Fast Transfer Navigation for Autonomous Robots |
| title_full | Fast Transfer Navigation for Autonomous Robots |
| title_fullStr | Fast Transfer Navigation for Autonomous Robots |
| title_full_unstemmed | Fast Transfer Navigation for Autonomous Robots |
| title_short | Fast Transfer Navigation for Autonomous Robots |
| title_sort | fast transfer navigation for autonomous robots |
| url | http://dx.doi.org/10.1155/2021/3028319 |
| work_keys_str_mv | AT chenwang fasttransfernavigationforautonomousrobots AT xudongli fasttransfernavigationforautonomousrobots AT xiaolintao fasttransfernavigationforautonomousrobots AT kailing fasttransfernavigationforautonomousrobots AT quhuiliu fasttransfernavigationforautonomousrobots AT gantao fasttransfernavigationforautonomousrobots |