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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2404.10446 |
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| _version_ | 1866913337864880128 |
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| author | Gadd, Matthew De Martini, Daniele Pitt, Luke Tubby, Wayne Towlson, Matthew Prahacs, Chris Bartlett, Oliver Jackson, John Qi, Man Newman, Paul Hector, Andrew Salguero-Gómez, Roberto Hawes, Nick |
| author_facet | Gadd, Matthew De Martini, Daniele Pitt, Luke Tubby, Wayne Towlson, Matthew Prahacs, Chris Bartlett, Oliver Jackson, John Qi, Man Newman, Paul Hector, Andrew Salguero-Gómez, Roberto Hawes, Nick |
| contents | We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform, as localisation is a foundational part of that control loop, and so routes must be carefully taught and retaught until autonomy is robust and repeatable. Our system is demonstrated over a 6-week period monitoring the response of grass species to experimental climate change manipulations. We also discuss the applicability of our pipeline to monitor biodiversity in other complex natural settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10446 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring Gadd, Matthew De Martini, Daniele Pitt, Luke Tubby, Wayne Towlson, Matthew Prahacs, Chris Bartlett, Oliver Jackson, John Qi, Man Newman, Paul Hector, Andrew Salguero-Gómez, Roberto Hawes, Nick Robotics We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform, as localisation is a foundational part of that control loop, and so routes must be carefully taught and retaught until autonomy is robust and repeatable. Our system is demonstrated over a 6-week period monitoring the response of grass species to experimental climate change manipulations. We also discuss the applicability of our pipeline to monitor biodiversity in other complex natural settings. |
| title | Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.10446 |