SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910859072110592 |
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| author | Li, Xiaoying Xu, Long Huang, Xiaolin Xue, Donglai Zhang, Zhihao Han, Zhichao Xu, Chao Cao, Yanjun Gao, Fei |
| author_facet | Li, Xiaoying Xu, Long Huang, Xiaolin Xue, Donglai Zhang, Zhihao Han, Zhichao Xu, Chao Cao, Yanjun Gao, Fei |
| contents | Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain Li, Xiaoying Xu, Long Huang, Xiaolin Xue, Donglai Zhang, Zhihao Han, Zhichao Xu, Chao Cao, Yanjun Gao, Fei Robotics Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver. |
| title | SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.02412 |