论文标题
现场农业机器人动态天然植物的可再现修剪系统
Reproducible Pruning System on Dynamic Natural Plants for Field Agricultural Robots
论文作者
论文摘要
修剪是切割不良和不健康的植物分支的艺术，并且是现场机器人技术中的艰巨任务之一。当植物树枝移动时，它变得更加复杂。此外，由于葡萄园中葡萄藤的异质性质，机器人修剪技能的可重复性是应对的另一个挑战。这项研究提出了一个多模式框架来处理动态葡萄藤，目的是SIM2REAL技能转移。葡萄藤的3D模型是在搅拌机发动机中构建的，并在模拟环境中渲染，以训练机器人。自然入口控制器（NAC）用于处理葡萄藤的动力学。它使用力反馈并补偿摩擦效应，同时保持系统的被动性。更快的R-CNN用于检测藤蔓上的马刺，然后使用K-均值聚类的统计模式识别算法用于查找有效的修剪点。提出的框架在模拟和真实环境中进行了测试。
Pruning is the art of cutting unwanted and unhealthy plant branches and is one of the difficult tasks in the field robotics. It becomes even more complex when the plant branches are moving. Moreover, the reproducibility of robot pruning skills is another challenge to deal with due to the heterogeneous nature of vines in the vineyard. This research proposes a multi-modal framework to deal with the dynamic vines with the aim of sim2real skill transfer. The 3D models of vines are constructed in blender engine and rendered in simulated environment as a need for training the robot. The Natural Admittance Controller (NAC) is applied to deal with the dynamics of vines. It uses force feedback and compensates the friction effects while maintaining the passivity of system. The faster R-CNN is used to detect the spurs on the vines and then statistical pattern recognition algorithm using K-means clustering is applied to find the effective pruning points. The proposed framework is tested in simulated and real environments.
