We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle.

翻译：本文提出了一种用于安全高效穿冠层果实计数的自主空中系统。在大型果园中，基于果园布局、冠层密度和植株变异性的飞行路径微调复杂性，给空中机器人应用带来了重大挑战。穿冠层导航对于最小化枝叶遮挡至关重要，但与传统冠层上方飞行相比，其环境更为复杂密集，因此更具挑战性。我们的系统通过整合以下组件应对这些挑战：i) 用于优化飞行轨迹的高保真仿真框架，ii) 用于冠层级导航与数据采集的低成本自主软件栈，以及 iii) 利用RGB图像进行果实检测与计数的鲁棒工作流程。我们通过冠层级航拍图像的果实计数实验，并展示实验飞行器的自主导航能力，验证了所提方法的有效性。