Self-Localization Using Trajectory Attractors in Outdoor Environments
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- Yamane Ken
- Teikyo University
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- Akutsu Mitsunori
- Teikyo University
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Abstract
<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>
Journal
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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
Fuji Technology Press Ltd.
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Keywords
Details 詳細情報について
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- CRID
- 1390579996561022464
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- NII Book ID
- AA10809998
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- ISSN
- 18838049
- 09153942
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- NDL BIB ID
- 033224228
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
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- Abstract License Flag
- Disallowed