End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909885420011520 |
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| author | Mineiro, Ana Luiza Affonso, Francisco Becker, Marcelo |
| author_facet | Mineiro, Ana Luiza Affonso, Francisco Becker, Marcelo |
| contents | Reliable navigation in under-canopy agricultural environments remains a challenge due to GNSS unreliability, cluttered rows, and variable lighting. To address these limitations, we present an end-to-end learning-based navigation system that maps raw 3D LiDAR data directly to control commands using a deep reinforcement learning policy trained entirely in simulation. Our method includes a voxel-based downsampling strategy that reduces LiDAR input size by 95.83%, enabling efficient policy learning without relying on labeled datasets or manually designed control interfaces. The policy was validated in simulation, achieving a 100% success rate in straight-row plantations and showing a gradual decline in performance as row curvature increased, tested across varying sinusoidal frequencies and amplitudes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18608 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning Mineiro, Ana Luiza Affonso, Francisco Becker, Marcelo Robotics Artificial Intelligence Reliable navigation in under-canopy agricultural environments remains a challenge due to GNSS unreliability, cluttered rows, and variable lighting. To address these limitations, we present an end-to-end learning-based navigation system that maps raw 3D LiDAR data directly to control commands using a deep reinforcement learning policy trained entirely in simulation. Our method includes a voxel-based downsampling strategy that reduces LiDAR input size by 95.83%, enabling efficient policy learning without relying on labeled datasets or manually designed control interfaces. The policy was validated in simulation, achieving a 100% success rate in straight-row plantations and showing a gradual decline in performance as row curvature increased, tested across varying sinusoidal frequencies and amplitudes. |
| title | End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18608 |