面向安全高效的无人机穿冠自主果实计数研究
Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs
AI总结：
针对大规模果园中无人机穿冠果实计数的环境复杂、路径调优难等问题，研究整合高保真仿真框架、低成本自主导航栈与RGB果实计数工作流，实现安全高效的穿冠自主果实计数。
摘要：
我们提出了一套自主空中系统，用于安全高效的穿冠果实计数。由于需根据果园布局、冠层密度和植株差异性微调飞行路径的复杂度较高，空中机器人在大规模果园中的应用面临显著挑战。穿冠导航对减少叶片和枝条遮挡至关重要，但与传统的冠上飞行相比，其所处环境复杂且密集，难度更大。我们的系统通过整合三方面内容应对这些挑战：i）用于优化飞行轨迹的高保真仿真框架，ii）用于冠层级导航和数据采集的低成本自主技术栈，iii）利用RGB图像进行果实检测与计数的鲁棒工作流。我们通过冠层级航拍图像的果实计数任务，并展示实验载具的自主导航能力，验证了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
