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- Yamane Ken
- Teikyo University
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- Akutsu Mitsunori
- Teikyo University
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説明
<p>Self-localization in probabilistic robotics requires detailed, geographically consistent environmental maps, which increases the computational cost. In this study, we propose a simple self-localization method that does not require such maps. In the proposed method, the order structure, such as the mobile robot’s navigation route, is embedded as trajectory attractors in the state space of a nonmonotone neural network, and self-position estimation is performed by processing based on the autonomous dynamics of the network. From experiments, we demonstrated the basic performance of the proposed method, including robust self-localization in complex outdoor environments. Furthermore, self-localization is possible on multiple courses with overlapping paths by suitably varying the network dynamics based on environmental information. While issues remain, this study points to the great potential of neurodynamics-based robotic self-localization.</p>
収録刊行物
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- Journal of Robotics and Mechatronics
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Journal of Robotics and Mechatronics 35 (6), 1435-1449, 2023-12-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390579996561022464
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- NII書誌ID
- AA10809998
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- ISSN
- 18838049
- 09153942
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- NDL書誌ID
- 033224228
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可