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.
@article{arxiv.2509.18608,
title = {End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning},
author = {Ana Luiza Mineiro and Francisco Affonso and Marcelo Becker},
journal= {arXiv preprint arXiv:2509.18608},
year = {2025}
}
Comments
Accepted to the 22nd International Conference on Advanced Robotics (ICAR 2025). 7 pages