Soil Sample Search in Partially Observable Environments
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917740646760448 |
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| author | Yang, Han Dudash, Andrew |
| author_facet | Yang, Han Dudash, Andrew |
| contents | To work in unknown outdoor environments, autonomous sampling machines need the ability to target samples despite limited visibility and robotic arm reach distance. We design a heuristic guided search method to speed up the search process and more efficiently localize the approximate center of soil regions. Through simulation experiments, we assess the effectiveness of the proposed algorithm and discover superior performance in terms of speed, distance traveled, and success rate compared to naive baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01589 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soil Sample Search in Partially Observable Environments Yang, Han Dudash, Andrew Robotics To work in unknown outdoor environments, autonomous sampling machines need the ability to target samples despite limited visibility and robotic arm reach distance. We design a heuristic guided search method to speed up the search process and more efficiently localize the approximate center of soil regions. Through simulation experiments, we assess the effectiveness of the proposed algorithm and discover superior performance in terms of speed, distance traveled, and success rate compared to naive baselines. |
| title | Soil Sample Search in Partially Observable Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.01589 |