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

Abstract

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.

References in corpus (6)

• Learning Quadrupedal Locomotion over Challenging Terrain
• RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
• Learning High-Speed Flight in the Wild
• Human-Piloted Drone Racing: Visual Processing and Control
• A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards
• A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning