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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.18293 |
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| _version_ | 1866913843204063232 |
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| author | Yang, Teaya Ibrahimov, Roman Mueller, Mark W. |
| author_facet | Yang, Teaya Ibrahimov, Roman Mueller, Mark W. |
| contents | We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs Yang, Teaya Ibrahimov, Roman Mueller, Mark W. Robotics We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle. |
| title | Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.18293 |