Vision-based Navigation of Unmanned Aerial Vehicles in Orchards: An Imitation Learning Approach

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. 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. Introduction Unmanned aerial vehicle (UAV) technology has made significant progress in recent years, particularly for applications in agriculture. The ability to navigate within orchard rows allows UAVs to perform tasks such as crop inspection and yield estimation (Zhang et al., 2021). This capability provides a valuable tool for remote sensing and precision agriculture (Chen et al., 2022), leading to more efficient and improved orchard management. However, most existing UAVs still depend on GPS for navigation in agricultural settings. This reliance limits their ability to operate in confined orchard rows, where dense tree canopies can block GPS signals. Additionally, in environments with unknown obstacles, such as tree branches in orchard rows, human pilots are frequently queried to provide avoidance maneuvers, which significantly increases their workload. The ability to navigate autonomously and safely in orchard scenes with weak GPS signals and obstacles presents several challenges and largely hinders the deployment of UAVs in orchard operations. Corresponding author Email address: zdkong@ucdavis.edu The view of the onboard camera is provided. When the GPS signal is attenuated, the UAV may rely on exteroceptive sensors to sense the environment and navigate. Advanced techniques to enable UAV autonomous operations without GPS include: 1) lidar-based, and 2) camera-based approaches.