End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2509.18608v1
• Date: Tue, 23 Sep 2025 03:56:10 GMT
• Title: End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning
• Authors: Ana Luiza Mineiro, Francisco Affonso, Marcelo Becker,
• Abstract summary: 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.<n>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.
• Score: 0.4588028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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