Terrain-aware Low Altitude Path Planning
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909657951371264 |
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| author | Jia, Yixuan Tagliabue, Andrea Thomas, Annika Tehrani, Navid Dadkhah How, Jonathan P. |
| author_facet | Jia, Yixuan Tagliabue, Andrea Thomas, Annika Tehrani, Navid Dadkhah How, Jonathan P. |
| contents | In this paper, we study the problem of generating low-altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behavior cloning and self-supervised learning, where the self-supervision component allows the learned policy to refine the paths generated by the expert planner. Simulation studies show 24.7% reduction in average path elevation compared to the standard behavior cloning approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Terrain-aware Low Altitude Path Planning Jia, Yixuan Tagliabue, Andrea Thomas, Annika Tehrani, Navid Dadkhah How, Jonathan P. Robotics In this paper, we study the problem of generating low-altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behavior cloning and self-supervised learning, where the self-supervision component allows the learned policy to refine the paths generated by the expert planner. Simulation studies show 24.7% reduction in average path elevation compared to the standard behavior cloning approach. |
| title | Terrain-aware Low Altitude Path Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.07141 |