机器人自动采集果园叶片光谱数据，解决人力短缺难题。
RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing
- 六自由度机械臂结合视觉系统，实现叶位精准定位与抓取。
- 实验室和田间采样成功率分别达95%和70%，验证系统可靠性。
- 开源设计适合农业自动化研究者快速复现与改进。
为应对劳动力短缺并满足不断增长的人口需求，机器人自动化已成为精准农业的关键。叶级高光谱光谱学在表型分析、作物健康监测、营养元素识别及病害与水分胁迫检测中表现优异。本文提出RoMu4o，一种用于果园作业的机器人操作单元，实现近距高光谱叶片感知的自动化。该地面机器人配备6自由度机械臂与视觉系统，支持实时深度学习图像处理与运动规划。我们构建了鲁棒的感知与操作流程，使机器人能成功抓取目标叶片并完成光谱测量。这些框架协同工作，从丛生叶中识别提取3D叶结构，提出6维姿态，并生成无碰撞的约束感知路径以实现精确操作。机械臂末端执行器集成了独立光源与高光谱传感器，提升数据保真度并简化校准流程。该机器人专为非结构化果园环境设计，系统性能在室内与室外植物模型中评估。1-LPB高光谱采样在实验室测试中达到95%成功率，田间试验为79%；在开心果果园中，自主抓取与光谱测量总体成功率70%。代码已开源：https://github.com/mehradmrt/UCM-AgBot-ROS2
原文摘要 · Abstract (English)
Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients within plants as well as detecting diseases and water stress. This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing. This ground robot is equipped with a 6DOF robotic arm and vision system for real-time deep learning-based image processing and motion planning. We developed robust perception and manipulation pipelines that enable the robot to successfully grasp target leaves and perform spectroscopy. These frameworks operate synergistically to identify and extract the 3D structure of leaves from an observed batch of foliage, propose 6D poses, and generate collision-free constraint-aware paths for precise leaf manipulation. The end-effector of the arm features a compact design that integrates an independent lighting source with a hyperspectral sensor, enabling high-fidelity data acquisition while streamlining the calibration process for accurate measurements. Our ground robot is engineered to operate in unstructured orchard environments. However, the performance of the system is evaluated in both indoor and outdoor plant models. The system demonstrated reliable performance for 1-LPB hyperspectral sampling, achieving 95% success rate in lab trials and 79% in field trials. Field experiments revealed an overall success rate of 70% for autonomous leaf grasping and hyperspectral measurement in a pistachio orchard. The open-source repository is available at: https://github.com/mehradmrt/UCM-AgBot-ROS2
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