Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot
3D spatial perception is one of the most important abilities for autonomous mobile robots. In environments with unknown objects, the ability to perform a local planner, which modifies the global path based on the perception results, is also required as an indispensable capability. In this paper, we...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10753613/ |
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| author | Koki Ozasa Yuichiro Toda Yoshimasa Nakamura Toshiki Masuda Hirohide Konishi Takayuki Matsuno |
| author_facet | Koki Ozasa Yuichiro Toda Yoshimasa Nakamura Toshiki Masuda Hirohide Konishi Takayuki Matsuno |
| author_sort | Koki Ozasa |
| collection | DOAJ |
| description | 3D spatial perception is one of the most important abilities for autonomous mobile robots. In environments with unknown objects, the ability to perform a local planner, which modifies the global path based on the perception results, is also required as an indispensable capability. In this paper, we propose a method based on Growing Neural Gas with Different Topologies (GNG-DT), which can be applied to unknown data, as a method for 3D spatial perception and local planner in unknown environments. First, we propose a method for extracting travelability perceptions from the features estimated by the topological structure of the GNG-DT. Next, we learn the topological structure of passability information based on the size of the robot from the extracted traversability percepts. Furthermore, we propose a local planner that uses the topological structure of traversability and passability learned from the point cloud currently perceived by the robot. In the experiments, we compared the cases where only traversability was used and where passability information was used in actual environments, and showed that the proposed method can plan a route that determines the area that the robot can actually pass through. |
| format | Article |
| id | doaj-art-02aeb041c236414cb83285faa2d76bf5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-02aeb041c236414cb83285faa2d76bf52024-11-23T00:01:29ZengIEEEIEEE Access2169-35362024-01-011217182417183510.1109/ACCESS.2024.349936410753613Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile RobotKoki Ozasa0Yuichiro Toda1https://orcid.org/0000-0003-4170-2300Yoshimasa Nakamura2https://orcid.org/0009-0001-1684-721XToshiki Masuda3Hirohide Konishi4Takayuki Matsuno5Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, JapanGraduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, JapanTokyo Metropolitan Industrial Technology Research Institute, Tokyo, JapanTokyo Metropolitan Industrial Technology Research Institute, Tokyo, JapanNSK Ltd., Tokyo, JapanGraduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, Japan3D spatial perception is one of the most important abilities for autonomous mobile robots. In environments with unknown objects, the ability to perform a local planner, which modifies the global path based on the perception results, is also required as an indispensable capability. In this paper, we propose a method based on Growing Neural Gas with Different Topologies (GNG-DT), which can be applied to unknown data, as a method for 3D spatial perception and local planner in unknown environments. First, we propose a method for extracting travelability perceptions from the features estimated by the topological structure of the GNG-DT. Next, we learn the topological structure of passability information based on the size of the robot from the extracted traversability percepts. Furthermore, we propose a local planner that uses the topological structure of traversability and passability learned from the point cloud currently perceived by the robot. In the experiments, we compared the cases where only traversability was used and where passability information was used in actual environments, and showed that the proposed method can plan a route that determines the area that the robot can actually pass through.https://ieeexplore.ieee.org/document/10753613/Autonomous mobile robotgrowing neural gaslocal planner |
| spellingShingle | Koki Ozasa Yuichiro Toda Yoshimasa Nakamura Toshiki Masuda Hirohide Konishi Takayuki Matsuno Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot IEEE Access Autonomous mobile robot growing neural gas local planner |
| title | Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot |
| title_full | Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot |
| title_fullStr | Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot |
| title_full_unstemmed | Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot |
| title_short | Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot |
| title_sort | passability based local planner using growing neural gas for an autonomous mobile robot |
| topic | Autonomous mobile robot growing neural gas local planner |
| url | https://ieeexplore.ieee.org/document/10753613/ |
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