用视觉引导机器人精准抓握并振动授粉，提升温室作物自动化效率。
Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
- 结合3D植物重建与弹性杆模型，规划无碰撞抓握位姿
- 92.5%主茎抓取成功率，振动参数经仿真优化确保不伤花
- 首个融合视觉抓取与振动建模的自动授粉机器人系统
机器人授粉为受控农业中缺乏风力授粉及商业传粉昆虫使用受限的场景提供了高效替代方案。本文提出并验证了一种基于视觉引导的机器人框架，利用安装在末端执行器上的RGB-D传感器数据，融合3D植物重建、目标抓握规划与物理驱动的振动建模，实现精准授粉。首先，通过3D重建将植物注册至机器人坐标系，识别主茎上无障碍的抓握位姿；其次，采用离散弹性杆模型预测驱动参数与花朵动态的关系，指导最优授粉策略选择；最后，配备软夹爪的机械臂抓握茎秆并施加可控振动以触发花粉释放。端到端实验显示主茎抓取成功率达92.5%，仿真优化进一步验证了该方法可行性，确保机器人在不损伤花朵的前提下安全高效完成授粉。据我们所知，这是首个联合集成视觉引导抓握与振动建模的自动化精密授粉机器人系统。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。