Efficient Feature Mapping Using a Collaborative Team of AUVs
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910764851265536 |
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| author | Biggs, Benjamin Stilwell, Daniel J. Yetkin, Harun McMahon, James |
| author_facet | Biggs, Benjamin Stilwell, Daniel J. Yetkin, Harun McMahon, James |
| contents | We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19409 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Feature Mapping Using a Collaborative Team of AUVs Biggs, Benjamin Stilwell, Daniel J. Yetkin, Harun McMahon, James Robotics We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources. |
| title | Efficient Feature Mapping Using a Collaborative Team of AUVs |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.19409 |