dc.citation.conferencePlace |
UK |
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dc.citation.endPage |
10068 |
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dc.citation.startPage |
10061 |
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dc.citation.title |
2023 IEEE International Conference on Robotics and Automation, ICRA 2023 |
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dc.contributor.author |
Lee, Hojin |
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dc.contributor.author |
Kwon, Junsung |
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dc.contributor.author |
Kwon, Cheolhyeon |
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dc.date.accessioned |
2024-01-03T17:35:19Z |
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dc.date.available |
2024-01-03T17:35:19Z |
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dc.date.created |
2024-01-03 |
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dc.date.issued |
2023-06-01 |
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dc.description.abstract |
This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicle-terrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU). |
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dc.identifier.bibliographicCitation |
2023 IEEE International Conference on Robotics and Automation, ICRA 2023, pp.10061 - 10068 |
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dc.identifier.doi |
10.1109/ICRA48891.2023.10161543 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/67657 |
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dc.language |
영어 |
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dc.publisher |
Institute of Electrical and Electronics Engineers Inc. |
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dc.title |
Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2023-05-29 |
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