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

Abstract-- Reliable navigation in under-canopy agricultural environments remains a challenge due to GNSS unreliability, cluttered rows, and variable lighting. T o 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. Autonomous robots have seen significant growth in modern agriculture, particularly for under-canopy tasks such as plant phenotyping, crop row harvesting, and disease scouting. These applications require platforms that are not only compact and agile but also capable of accurately navigating between dense crop rows (Figure 1) [1]. However, reliable navigation in such environments remains an active area of research due to several challenges, including clutter and occlusions caused by narrow row spacing and the high visual variability introduced by different plant growth stages [2].