基于视觉与在线学习的已收获番茄串机器人抓取
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning
AI总结：
针对已收获番茄串自动化抓取难题，提出基于深度学习视觉系统与在线学习抓取位姿排序算法的方法，实现杂乱堆叠番茄串的可靠抓取，实验清除率100%且首次抓取成功率93%。
摘要：
目前，番茄串的称重与包装需要大量人工操作。自动化的主要障碍在于难以开发可靠的机器人抓取系统来处理已收获的番茄串。我们提出一种方法，用于抓取杂乱堆叠在箱中的番茄串——这是收获后常见的存储与运输方式。该方法包含基于深度学习的视觉系统，首先识别箱中单个番茄串，再确定果梗上合适的抓取位置。为此，我们引入具有在线学习能力的抓取位姿排序算法。选择最具前景的抓取位姿后，机器人无需触觉传感器或几何模型即可执行捏取抓取。使用配备手眼RGB-D相机的机械臂进行实验室实验，结果显示从堆叠中拾取所有番茄串时清除率达100%；93%的番茄串首次尝试即成功抓取，剩余7%需多次尝试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
