Robotic pollination offers a promising alternative to manual labor and bumblebee-assisted methods in controlled agriculture, where wind-driven pollination is absent and regulatory restrictions limit the use of commercial pollinators. In this work, we present and validate a vision-guided robotic framework that uses data from an end-effector mounted RGB-D sensor and combines 3D plant reconstruction, targeted grasp planning, and physics-based vibration modeling to enable precise pollination. First, the plant is reconstructed in 3D and registered to the robot coordinate frame to identify obstacle-free grasp poses along the main stem. Second, a discrete elastic rod model predicts the relationship between actuation parameters and flower dynamics, guiding the selection of optimal pollination strategies. Finally, a manipulator with soft grippers grasps the stem and applies controlled vibrations to induce pollen release. End-to-end experiments demonstrate a 92.5\% main-stem grasping success rate, and simulation-guided optimization of vibration parameters further validates the feasibility of our approach, ensuring that the robot can safely and effectively perform pollination without damaging the flower. To our knowledge, this is the first robotic system to jointly integrate vision-based grasping and vibration modeling for automated precision pollination.

翻译：在可控农业环境中，由于缺乏风媒授粉条件且商业传粉媒介的使用受到法规限制，机器人授粉为替代人工劳动和熊蜂辅助方法提供了前景广阔的解决方案。本研究提出并验证了一种视觉引导的机器人框架，该框架利用末端执行器搭载的RGB-D传感器数据，结合三维植物重建、目标抓取规划及基于物理的振动建模，实现了精准授粉。首先，通过三维重建植物并将其配准至机器人坐标系，以识别主茎上无障碍的抓取位姿。其次，采用离散弹性杆模型预测驱动参数与花朵动力学之间的关系，从而指导最优授粉策略的选择。最后，配备软体夹爪的机械臂抓取茎杆并施加受控振动以诱导花粉释放。端到端实验表明，主茎抓取成功率达92.5%，而基于仿真的振动参数优化进一步验证了本方法的可行性，确保机器人能够安全高效地完成授粉且不损伤花朵。据我们所知，这是首个将视觉引导抓取与振动建模相结合用于自动化精准授粉的机器人系统。