Currently, truss tomato weighing and packaging require significant manual work. The main obstacle to automation lies in the difficulty of developing a reliable robotic grasping system for already harvested trusses. We propose a method to grasp trusses that are stacked in a crate with considerable clutter, which is how they are commonly stored and transported after harvest. The method consists of a deep learning-based vision system to first identify the individual trusses in the crate and then determine a suitable grasping location on the stem. To this end, we have introduced a grasp pose ranking algorithm with online learning capabilities. After selecting the most promising grasp pose, the robot executes a pinch grasp without needing touch sensors or geometric models. Lab experiments with a robotic manipulator equipped with an eye-in-hand RGB-D camera showed a 100% clearance rate when tasked to pick all trusses from a pile. 93% of the trusses were successfully grasped on the first try, while the remaining 7% required more attempts.

翻译：目前，串收番茄的称重与包装仍需大量人工操作。自动化面临的主要障碍在于难以开发出能够可靠抓取已收获番茄串的机器人系统。我们提出了一种抓取方法，用于处理堆放在周转箱中高度杂乱的番茄串——这正是收获后常见的存储和运输方式。该方法首先基于深度学习的视觉系统识别箱内单个番茄串，并在其茎秆上确定合适的抓取位置。为此，我们引入了一种具备在线学习能力的抓取姿态排序算法。在选定最优抓取姿态后，机器人无需触觉传感器或几何模型即可执行捏合抓取。采用配备眼在手（eye-in-hand）RGB-D相机的机器人操纵器进行实验室实验，当需要从一堆番茄串中抓取全部果实时，清空率达到100%。其中93%的番茄串首次尝试即成功抓取，剩余7%需要更多尝试。