用激光雷达和强化学习实现农田行间自动导航，无需依赖卫星定位。
End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning
- 直接将3D激光数据映射为控制指令，端到端训练。
- 仿真中直行田块成功率100%，曲率增大时性能渐降。
- 大幅降低点云数据量95.83%，无需标注数据或人工设计控制逻辑。
在作物冠层下环境进行可靠导航仍具挑战，原因包括GNSS不可靠、行间杂乱及光照变化。为此，我们提出一种基于深度强化学习的端到端导航系统，直接将原始3D LiDAR数据映射为控制命令，所有策略均在仿真中训练完成。方法采用体素化下采样策略，使LiDAR输入规模减少95.83%，从而实现高效策略学习，且不依赖标注数据或人工设计控制接口。该策略在仿真中验证，直行种植区成功率达100%，随着行道曲率增加（测试不同正弦频率与振幅），性能呈渐进下降趋势。
原文摘要 · Abstract (English)
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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