用视觉和模仿学习让无人机在果园自动导航避障
Vision-based Navigation of Unmanned Aerial Vehicles in Orchards: An Imitation Learning Approach
- 用变分自编码器+人类示范训练视觉运动策略
- 少量训练后即可自主飞行，避障能力强且少需人工干预
- 适合果园巡检等精准农业场景，泛化性好
果园中无人飞行器（UAV）的自主导航因障碍物多、无GPS信号而面临巨大挑战。本文提出一种基于视觉的学习方法，实现果园行间无人机的自主导航。采用基于变分自编码器（VAE）的控制器，通过干预式学习框架，从人类操作经验中学习视觉-运动策略。在自建四旋翼平台的真实果园环境中验证，仅需数次训练迭代，该控制器即可基于前视摄像头流实现自主飞行。实验表明，其具备出色的避障能力，飞行距离更长，对人工干预依赖更少，性能优于现有算法。此外，策略在新环境和不同速度条件下均表现出良好泛化能力。本研究不仅提升了无人机自主性，也为精准农业中的果园监测与管理提供了高效解决方案。
原文摘要 · Abstract (English)
Autonomous unmanned aerial vehicle (UAV) navigation in orchards presents significant challenges due to obstacles and GPS-deprived environments. In this work, we introduce a learning-based approach to achieve vision-based navigation of UAVs within orchard rows. Our method employs a variational autoencoder (VAE)-based controller, trained with an intervention-based learning framework that allows the UAV to learn a visuomotor policy from human experience. We validate our approach in real orchard environments with a custom-built quadrotor platform. Field experiments demonstrate that after only a few iterations of training, the proposed VAE-based controller can autonomously navigate the UAV based on a front-mounted camera stream. The controller exhibits strong obstacle avoidance performance, achieves longer flying distances with less human assistance, and outperforms existing algorithms. Furthermore, we show that the policy generalizes effectively to novel environments and maintains competitive performance across varying conditions and speeds. This research not only advances UAV autonomy but also holds significant potential for precision agriculture, improving efficiency in orchard monitoring and management.
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